Facilitating custom content extraction rule configuration for remote capture agents

ABSTRACT

The disclosed embodiments provide a system for extracting custom content from network packets. During operation, the system receives a stream of packets. The system then parses packets in the stream to determine a protocol for each packet. Next, the system applies a custom-content-extraction rule to each packet associated with a target protocol to obtain the extracted content. Then, the system stores the extracted content in events in a data store to facilitate subsequent queries involving the extracted content.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims benefit under 35 U.S.C. § 120 as a continuation of U.S. application Ser. No. 14/609,292, filed Jan. 29, 2015, the entire contents of which is hereby incorporated by reference as if fully set forth herein. The applicant(s) hereby rescind any disclaimer of claim scope in the parent application(s) or the prosecution history thereof and advise the USPTO that the claims in this application may be broader than any claim in the parent application(s).

BACKGROUND Field

The disclosed embodiments relate to techniques for processing network data. More specifically, the disclosed embodiments relate to techniques for extracting custom content from network packets.

Related Art

Over the past decade, virtualization has triggered a sea change in the field of network data capture. Almost every network capture product available today is a physical hardware appliance that customers have to purchase and configure. In addition, most network data capture technologies are built from scratch to serve a specific purpose and address the needs of a particular market. For example, network capture systems may be customized to extract data for security and intrusion-detection purposes, collect network performance data, facilitate Quality of Service (QoS) policies, redirect data, block network traffic or perform other analysis or management of network traffic. However, such targeted and fixed implementations of network capture technologies are not designed to be easily modifiable to address different business needs.

One challenge in generalizing a network capture system is to handle large volumes of network data. A network capture system typically monitors a large number packet streams at various locations in a network. Moreover, each packet stream can potentially run at gigabit clock rates, and as a consequence can generate tremendous volumes of data. It is theoretically possible to store all of this network data for subsequent analysis. However, as a practical matter it is infeasible and prohibitively expensive to store such large volumes of network data. Also, much of the data contained in packets is uninteresting; only specific packet fields contain interesting data for a particular purpose.

Hence, what is needed is a system for capturing network data that facilitates selectively retrieving specific data from packets.

SUMMARY

The disclosed embodiments provide a system for extracting custom content from network packets. During operation, the system receives a stream of packets. The system then parses packets in the stream to determine a protocol for each packet. Next, the system applies a custom-content-extraction rule to each packet associated with a target protocol to obtain the extracted content. Then, the system stores the extracted content in events in a data store to facilitate subsequent queries involving the extracted content.

In some embodiments, the system receives a query to be applied to the events in the data store. In response to this query, the system retrieves events from the data store, and uses a late-binding schema generated from the query to retrieve data values from the events. The system then processes the query using the retrieved data values.

In some embodiments, a rule for determining the target protocol is different than the custom-content-extraction rule that is used to obtain the extracted content from each packet that matches the target protocol.

In some embodiments, applying the custom-content-extraction rule to a given packet includes applying a field-specific regular expression to one or more target fields in the packet.

In some embodiments, applying the custom-content-extraction rule to a given packet includes applying a field-specific extraction rule for a structured data format, which can include XML or JSON, to one or more target fields in the packet.

In some embodiments, parsing packets in the stream to determine a protocol for each packet includes using a deep-packet inspection engine to determine the protocol for each packet.

In some embodiments, the system is additionally configured to obtain the custom-content-extraction rule from a user through a user interface.

In some embodiments, the user interface comprises a dialog box including fields for receiving input from a user, wherein the input includes one or more of: (1) a source field identifier that identifies a source field in each packet from which to obtain the extracted content; (2) an extraction rule type that specifies a type of extraction rule to be used to obtain the extracted content; (3) an extraction rule entered by the user; and (4) an identifier that is used to identify the extracted content.

In some embodiments, the custom context extraction is performed by a remote capture agent located on a network interface on a network. In these embodiments, storing the extracted content in the data store involves streaming the events from the remote capture agent to the data store.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a block diagram of an exemplary event-processing system in accordance with the disclosed embodiments.

FIG. 2 presents a flowchart illustrating how indexers process, index, and store data received from forwarders in accordance with the disclosed embodiments.

FIG. 3 presents a flowchart illustrating how a search head and indexers perform a search query in accordance with the disclosed embodiments.

FIG. 4 presents a block diagram of a system for processing search requests that uses extraction rules for field values in accordance with the disclosed embodiments.

FIG. 5 illustrates an exemplary search query received from a client and executed by search peers in accordance with the disclosed embodiments.

FIG. 6A illustrates a search screen in accordance with the disclosed embodiments.

FIG. 6B illustrates a data summary dialog that enables a user to select various data sources in accordance with the disclosed embodiments.

FIG. 7A illustrates a key indicators view in accordance with the disclosed embodiments.

FIG. 7B illustrates an incident review dashboard in accordance with the disclosed embodiments.

FIG. 7C illustrates a proactive monitoring tree in accordance with the disclosed embodiments.

FIG. 7D illustrates a screen displaying both log data and performance data in accordance with the disclosed embodiments.

FIG. 8 shows a schematic of a system in accordance with the disclosed embodiments.

FIG. 9A shows a remote capture agent in accordance with the disclosed embodiments.

FIG. 9B shows the protocol-based capture of network data using a remote capture agent in accordance with the disclosed embodiments.

FIG. 10 shows a configuration server in accordance with the disclosed embodiments.

FIG. 11A shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 11B shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 11C shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 11D shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 11E shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 11F shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 12A shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 12B shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 13 shows a flowchart illustrating the processing of network data in accordance with the disclosed embodiments.

FIG. 14 shows a flowchart illustrating the process of using configuration information associated with a protocol classification to build an event stream from a packet flow in accordance with the disclosed embodiments.

FIG. 15 shows a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments.

FIG. 16 shows a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments.

FIG. 17A shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 17B shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 17C shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 17D shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 17E shows an exemplary screenshot in accordance with the disclosed embodiments.

FIG. 18 shows a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments.

FIG. 19 shows a flowchart illustrating the process of displaying event stream information represented by a grouping of the event streams by an event stream attribute in accordance with the disclosed embodiments.

FIG. 20 presents a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments.

FIG. 21 presents a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments.

FIG. 22 presents a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments.

FIG. 23A presents a block diagram illustrating a system that facilitates custom content extraction in accordance with the disclosed embodiments.

FIG. 23B illustrates how extracted content obtained from a packet is incorporated into an event in accordance with the disclosed embodiments.

FIG. 24 presents a flowchart illustrating how the packets are processed to facilitate custom content extraction in accordance with the disclosed embodiments.

FIG. 25 presents a flowchart illustrating how a query is processed in accordance with the disclosed embodiments.

FIG. 26 illustrates a dialog box that is configured to receive a custom-content-extraction rule in accordance with the disclosed embodiments.

FIG. 27 shows a computer system in accordance with the disclosed embodiments.

In the figures, like reference numerals refer to the same figure elements.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled in the art to make and use the embodiments, and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present invention is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

The data structures and code described in this detailed description are typically stored on a computer-readable storage medium, which may be any device or medium that can store code and/or data for use by a computer system. The computer-readable storage medium includes, but is not limited to, volatile memory, non-volatile memory, magnetic and optical storage devices such as disk drives, magnetic tape, CDs (compact discs), DVDs (digital versatile discs or digital video discs), or other media capable of storing code and/or data now known or later developed.

The methods and processes described in the detailed description section can be embodied as code and/or data, which can be stored in a computer-readable storage medium as described above. When a computer system reads and executes the code and/or data stored on the computer-readable storage medium, the computer system performs the methods and processes embodied as data structures and code and stored within the computer-readable storage medium.

Furthermore, methods and processes described herein can be included in hardware modules or apparatus. These modules or apparatus may include, but are not limited to, an application-specific integrated circuit (ASIC) chip, a field-programmable gate array (FPGA), a dedicated or shared processor that executes a particular software module or a piece of code at a particular time, and/or other programmable-logic devices now known or later developed. When the hardware modules or apparatus are activated, they perform the methods and processes included within them.

1.1 Overview

Modern data centers often comprise thousands of host computer systems that operate collectively to service requests from even larger numbers of remote clients. During operation, these data centers generate significant volumes of performance data and diagnostic information that can be analyzed to quickly diagnose performance problems. In order to reduce the size of this performance data, the data is typically pre-processed prior to being stored based on anticipated data-analysis needs. For example, pre-specified data items can be extracted from the performance data and stored in a database to facilitate efficient retrieval and analysis at search time. However, the rest of the performance data is not saved and is essentially discarded during pre-processing. As storage capacity becomes progressively cheaper and more plentiful, there are fewer incentives to discard this performance data and many reasons to keep it.

This plentiful storage capacity is presently making it feasible to store massive quantities of minimally processed performance data at “ingestion time” for later retrieval and analysis at “search time.” Note that performing the analysis operations at search time provides greater flexibility because it enables an analyst to search all of the performance data, instead of searching pre-specified data items that were stored at ingestion time. This enables the analyst to investigate different aspects of the performance data instead of being confined to the pre-specified set of data items that was selected at ingestion time.

However, analyzing massive quantities of heterogeneous performance data at search time can be a challenging task. A data center may generate heterogeneous performance data from thousands of different components, which can collectively generate tremendous volumes of performance data that can be time-consuming to analyze. For example, this performance data can include data from system logs, network packet data, sensor data, and data generated by various applications. Also, the unstructured nature of much of this performance data can pose additional challenges because of the difficulty of applying semantic meaning to unstructured data, and the difficulty of indexing and querying unstructured data using traditional database systems.

These challenges can be addressed by using an event-based system, such as the SPLUNK® ENTERPRISE system produced by Splunk Inc. of San Francisco, Calif., to store and process performance data. The SPLUNK® ENTERPRISE system is the leading platform for providing real-time operational intelligence that enables organizations to collect, index, and harness machine-generated data from various websites, applications, servers, networks, and mobile devices that power their businesses. The SPLUNK® ENTERPRISE system is particularly useful for analyzing unstructured performance data, which is commonly found in system log files. Although many of the techniques described herein are explained with reference to the SPLUNK® ENTERPRISE system, the techniques are also applicable to other types of data server systems.

In the SPLUNK® ENTERPRISE system, performance data is stored as “events,” wherein each event comprises a collection of performance data and/or diagnostic information that is generated by a computer system and is correlated with a specific point in time. Events can be derived from “time-series data,” wherein time-series data comprises a sequence of data points (e.g., performance measurements from a computer system) that are associated with successive points in time and are typically spaced at uniform time intervals. Events can also be derived from “structured” or “unstructured” data. Structured data has a predefined format, wherein specific data items with specific data formats reside at predefined locations in the data. For example, structured data can include data items stored in fields in a database table. In contrast, unstructured data does not have a predefined format. This means that unstructured data can comprise various data items having different data types that can reside at different locations. For example, when the data source is an operating system log, an event can include one or more lines from the operating system log containing raw data that includes different types of performance and diagnostic information associated with a specific point in time. Examples of data sources from which an event may be derived include, but are not limited to: web servers; application servers; databases; firewalls; routers; operating systems; and software applications that execute on computer systems, mobile devices, and sensors. The data generated by such data sources can be produced in various forms including, for example and without limitation, server log files, activity log files, configuration files, messages, network packet data, performance measurements and sensor measurements. An event typically includes a timestamp that may be derived from the raw data in the event, or may be determined through interpolation between temporally proximate events having known timestamps.

The SPLUNK® ENTERPRISE system also facilitates using a flexible schema to specify how to extract information from the event data, wherein the flexible schema may be developed and redefined as needed. Note that a flexible schema may be applied to event data “on the fly,” when it is needed (e.g., at search time), rather than at ingestion time of the data as in traditional database systems. Because the schema is not applied to event data until it is needed (e.g., at search time), it is referred to as a “late-binding schema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw data, which can include unstructured data, machine data, performance measurements or other time-series data, such as data obtained from weblogs, syslogs, or sensor readings. It divides this raw data into “portions,” and optionally transforms the data to produce timestamped events. The system stores the timestamped events in a data store, and enables a user to run queries against the data store to retrieve events that meet specified criteria, such as containing certain keywords or having specific values in defined fields. Note that the term “field” refers to a location in the event data containing a value for a specific data item.

As noted above, the SPLUNK® ENTERPRISE system facilitates using a late-binding schema while performing queries on events. A late-binding schema specifies “extraction rules” that are applied to data in the events to extract values for specific fields. More specifically, the extraction rules for a field can include one or more instructions that specify how to extract a value for the field from the event data. An extraction rule can generally include any type of instruction for extracting values from data in events. In some cases, an extraction rule comprises a regular expression, in which case the rule is referred to as a “regex rule.”

In contrast to a conventional schema for a database system, a late-binding schema is not defined at data ingestion time. Instead, the late-binding schema can be developed on an ongoing basis until the time at which a query is actually executed. This means that extraction rules for the fields in a query may be provided in the query itself, or may be located during execution of the query. Hence, as an analyst learns more about the data in the events, the analyst can continue to refine the late-binding schema by adding new fields, deleting fields, or changing the field extraction rules until the next time the schema is used by a query. Because the SPLUNK® ENTERPRISE system maintains the underlying raw data and provides a late-binding schema for searching the raw data, it enables an analyst to investigate questions that arise as the analyst learns more about the events.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured to automatically generate extraction rules for certain fields in the events when the events are being created, indexed, or stored, or possibly at a later time. Alternatively, a user may manually define extraction rules for fields using a variety of techniques.

Also, a number of “default fields” that specify metadata about the events rather than data in the events themselves can be created automatically. For example, such default fields can specify: a timestamp for the event data; a host from which the event data originated; a source of the event data; and a source type for the event data. These default fields may be determined automatically when the events are created, indexed or stored.

In some embodiments, a common field name may be used to reference two or more fields containing equivalent data items, even though the fields may be associated with different types of events that possibly have different data formats and different extraction rules. By enabling a common field name to be used to identify equivalent fields from different types of events generated by different data sources, the system facilitates use of a “common information model” (CIM) across the different data sources.

1.2 Data Server System

FIG. 1 shows a block diagram of an exemplary event-processing system 100, similar to the SPLUNK® ENTERPRISE system. System 100 includes one or more forwarders 101 that collect data obtained from a variety of different data sources 105, and one or more indexers 102 that store, process, and/or perform operations on this data, wherein each indexer operates on data contained in a specific data store 103. These forwarders and indexers can comprise separate computer systems in a data center, or may alternatively comprise separate processes executing on various computer systems in a data center.

During operation, forwarders 101 identify which indexers 102 will receive the collected data and then forward the data to the identified indexers 102. Forwarders 101 can also perform operations to strip extraneous data and detect timestamps in the data. Forwarders 101 may next determine which indexers 102 will receive each data item and forward the data items to the determined indexers 102. Indexers 102 may then provide the data for storage in one or more data stores 103.

As mentioned above, the data may include streams, logs, database records, messages, archives, and/or other records containing time-series data. Time-series data refers to any data that can be associated with a timestamp. The data can be structured, unstructured, or semi-structured and come from files or directories. Unstructured data may include data, such as machine data and web logs, that is not organized to facilitate extraction of values for fields from the data.

Note that distributing data across different indexers facilitates parallel processing. This parallel processing can take place at data ingestion time, because multiple indexers can process the incoming data in parallel. The parallel processing can also take place at search time, because multiple indexers can search the data in parallel.

System 100 and the processes described below with respect to FIGS. 1-5 are further described in “Exploring Splunk Search Processing Language (SPL) Primer and Cookbook,” by David Carasso, CITO Research, 2012, and in “Optimizing Data Analysis With a Semi-Structured Time-series Database,” by Ledion Bitincka, Archana Ganapathi, Stephen Sorkin, and Steve Zhang, SLAML, 2010, each of which is hereby incorporated herein by reference in its entirety for all purposes.

1.3 Data Ingestion

FIG. 2 presents a flowchart illustrating how an indexer processes, indexes, and stores data received from forwarders in accordance with the disclosed embodiments. At block 201, the indexer receives the data from the forwarder. Next, at block 202, the indexer apportions the data into events. Note that the data can include lines of text that are separated by carriage returns or line breaks and an event may include one or more of these lines. During the apportioning process, the indexer can use heuristic rules to automatically determine the boundaries of the events, which for example coincide with line boundaries. These heuristic rules may be determined based on the source of the data, wherein the indexer can be explicitly informed about the source of the data or can infer the source of the data by examining the data. These heuristic rules can include regular expression-based rules or delimiter-based rules for determining event boundaries, wherein the event boundaries may be indicated by predefined characters or character strings. These predefined characters may include punctuation marks or other special characters including, for example, carriage returns, tabs, spaces or line breaks. In some cases, a user can fine-tune or configure the rules that the indexers use to determine event boundaries in order to adapt the rules to the user's specific requirements.

Next, the indexer determines a timestamp for each event at block 203. As mentioned above, these timestamps can be determined by extracting the time directly from data in the event, or by interpolating the time based on timestamps from temporally proximate events. In some cases, a timestamp can be determined based on the time the data was received or generated. The indexer subsequently associates the determined timestamp with each event at block 204, for example by storing the timestamp as metadata for each event.

Then, the system can apply transformations to data to be included in events at block 205. For log data, such transformations can include removing a portion of an event (e.g., a portion used to define event boundaries, extraneous text, characters, etc.) or removing redundant portions of an event. Note that a user can specify portions to be removed using a regular expression or any other possible technique.

Next, a keyword index can optionally be generated to facilitate fast keyword searching for events. To build a keyword index, the indexer first identifies a set of keywords in block 206. Then, at block 207 the indexer includes the identified keywords in an index, which associates each stored keyword with references to events containing that keyword (or to locations within events where that keyword is located). When an indexer subsequently receives a keyword-based query, the indexer can access the keyword index to quickly identify events containing the keyword.

In some embodiments, the keyword index may include entries for name-value pairs found in events, wherein a name-value pair can include a pair of keywords connected by a symbol, such as an equals sign or colon. In this way, events containing these name-value pairs can be quickly located. In some embodiments, fields can automatically be generated for some or all of the name-value pairs at the time of indexing. For example, if the string “dest=10.0.1.2” is found in an event, a field named “dest” may be created for the event, and assigned a value of “10.0.1.2.”

Finally, the indexer stores the events in a data store at block 208, wherein a timestamp can be stored with each event to facilitate searching for events based on a time range. In some cases, the stored events are organized into a plurality of buckets, wherein each bucket stores events associated with a specific time range. This not only improves time-based searches, but it also allows events with recent timestamps that may have a higher likelihood of being accessed to be stored in faster memory to facilitate faster retrieval. For example, a bucket containing the most recent events can be stored as flash memory instead of on hard disk.

Each indexer 102 is responsible for storing and searching a subset of the events contained in a corresponding data store 103. By distributing events among the indexers and data stores, the indexers can analyze events for a query in parallel, for example using map-reduce techniques, wherein each indexer returns partial responses for a subset of events to a search head that combines the results to produce an answer for the query. By storing events in buckets for specific time ranges, an indexer may further optimize searching by looking only in buckets for time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across different indexers and data stores to facilitate high availability and disaster recovery as is described in U.S. patent application Ser. No. 14/266,812 filed on 30 Apr. 2014, and in U.S. patent application Ser. No. 14/266,817 also filed on 30 Apr. 2014.

1.4 Query Processing

FIG. 3 presents a flowchart illustrating how a search head and indexers perform a search query in accordance with the disclosed embodiments. At the start of this process, a search head receives a search query from a client at block 301. Next, at block 302, the search head analyzes the search query to determine what portions can be delegated to indexers and what portions need to be executed locally by the search head. At block 303, the search head distributes the determined portions of the query to the indexers. Note that commands that operate on single events can be trivially delegated to the indexers, while commands that involve events from multiple indexers are harder to delegate.

Then, at block 304, the indexers to which the query was distributed search their data stores for events that are responsive to the query. To determine which events are responsive to the query, the indexer searches for events that match the criteria specified in the query. This criteria can include matching keywords or specific values for certain fields. In a query that uses a late-binding schema, the searching operations in block 304 may involve using the late-binding schema to extract values for specified fields from events at the time the query is processed. Next, the indexers can either send the relevant events back to the search head, or use the events to calculate a partial result, and send the partial result back to the search head.

Finally, at block 305, the search head combines the partial results and/or events received from the indexers to produce a final result for the query. This final result can comprise different types of data depending upon what the query is asking for. For example, the final results can include a listing of matching events returned by the query, or some type of visualization of data from the returned events. In another example, the final result can include one or more calculated values derived from the matching events.

Moreover, the results generated by system 100 can be returned to a client using different techniques. For example, one technique streams results back to a client in real-time as they are identified. Another technique waits to report results to the client until a complete set of results is ready to return to the client. Yet another technique streams interim results back to the client in real-time until a complete set of results is ready, and then returns the complete set of results to the client. In another technique, certain results are stored as “search jobs,” and the client may subsequently retrieve the results by referencing the search jobs.

The search head can also perform various operations to make the search more efficient. For example, before the search head starts executing a query, the search head can determine a time range for the query and a set of common keywords that all matching events must include. Next, the search head can use these parameters to query the indexers to obtain a superset of the eventual results. Then, during a filtering stage, the search head can perform field-extraction operations on the superset to produce a reduced set of search results.

1.5 Field Extraction

FIG. 4 presents a block diagram illustrating how fields can be extracted during query processing in accordance with the disclosed embodiments. At the start of this process, a search query 402 is received at a query processor 404. Query processor 404 includes various mechanisms for processing a query, wherein these mechanisms can reside in a search head 104 and/or an indexer 102. Note that the exemplary search query 402 illustrated in FIG. 4 is expressed in Search Processing Language (SPL), which is used in conjunction with the SPLUNK® ENTERPRISE system. SPL is a pipelined search language in which a set of inputs is operated on by a first command in a command line, and then a subsequent command following the pipe symbol “1” operates on the results produced by the first command, and so on for additional commands. Search query 402 can also be expressed in other query languages, such as the Structured Query Language (“SQL”) or any suitable query language.

Upon receiving search query 402, query processor 404 sees that search query 402 includes two fields “IP” and “target.” Query processor 404 also determines that the values for the “IP” and “target” fields have not already been extracted from events in data store 414, and consequently determines that query processor 404 needs to use extraction rules to extract values for the fields. Hence, query processor 404 performs a lookup for the extraction rules in a rule base 406, wherein rule base 406 maps field names to corresponding extraction rules and obtains extraction rules 408-409, wherein extraction rule 408 specifies how to extract a value for the “IP” field from an event, and extraction rule 409 specifies how to extract a value for the “target” field from an event. As is illustrated in FIG. 4, extraction rules 408-409 can comprise regular expressions that specify how to extract values for the relevant fields. Such regular-expression-based extraction rules are also referred to as “regex rules.” In addition to specifying how to extract field values, the extraction rules may also include instructions for deriving a field value by performing a function on a character string or value retrieved by the extraction rule. For example, a transformation rule may truncate a character string, or convert the character string into a different data format. In some cases, the query itself can specify one or more extraction rules.

Next, query processor 404 sends extraction rules 408-409 to a field extractor 412, which applies extraction rules 408-409 to events 416-418 in a data store 414. Note that data store 414 can include one or more data stores, and extraction rules 408-409 can be applied to large numbers of events in data store 414, and are not meant to be limited to the three events 416-418 illustrated in FIG. 4. Moreover, the query processor 404 can instruct field extractor 412 to apply the extraction rules to all the events in a data store 414, or to a subset of the events that have been filtered based on some criteria.

Next, field extractor 412 applies extraction rule 408 for the first command “Search IP=“10*” to events in data store 414 including events 416-418. Extraction rule 408 is used to extract values for the IP address field from events in data store 414 by looking for a pattern of one or more digits, followed by a period, followed again by one or more digits, followed by another period, followed again by one or more digits, followed by another period, and followed again by one or more digits. Next, field extractor 412 returns field values 420 to query processor 404, which uses the criterion IP=“10*” to look for IP addresses that start with “10”. Note that events 416 and 417 match this criterion, but event 418 does not, so the result set for the first command is events 416-417.

Query processor 404 then sends events 416-417 to the next command “stats count target.” To process this command, query processor 404 causes field extractor 412 to apply extraction rule 409 to events 416-417. Extraction rule 409 is used to extract values for the target field for events 416-417 by skipping the first four commas in events 416-417, and then extracting all of the following characters until a comma or period is reached. Next, field extractor 412 returns field values 421 to query processor 404, which executes the command “stats count target” to count the number of unique values contained in the target fields, which in this example produces the value “2” that is returned as a final result 422 for the query.

Note that query results can be returned to a client, a search head, or any other system component for further processing. In general, query results may include: a set of one or more events; a set of one or more values obtained from the events; a subset of the values; statistics calculated based on the values; a report containing the values; or a visualization, such as a graph or chart, generated from the values.

1.6 Exemplary Search Screen

FIG. 6A illustrates an exemplary search screen 600 in accordance with the disclosed embodiments. Search screen 600 includes a search bar 602 that accepts user input in the form of a search string. It also includes a time range picker 612 that enables the user to specify a time range for the search. For “historical searches” the user can select a specific time range, or alternatively a relative time range, such as “today,” “yesterday” or “last week.” For “real-time searches,” the user can select the size of a preceding time window to search for real-time events. Search screen 600 also initially displays a “data summary” dialog as is illustrated in FIG. 6B that enables the user to select different sources for the event data, for example by selecting specific hosts and log files.

After the search is executed, the search screen 600 can display the results through search results tabs 604, wherein search results tabs 604 includes: an “events tab” that displays various information about events returned by the search; a “statistics tab” that displays statistics about the search results; and a “visualization tab” that displays various visualizations of the search results. The events tab illustrated in FIG. 6A displays a timeline 605 that graphically illustrates the number of events that occurred in one-hour intervals over the selected time range. It also displays an events list 608 that enables a user to view the raw data in each of the returned events. It additionally displays a fields sidebar 606 that includes statistics about occurrences of specific fields in the returned events, including “selected fields” that are pre-selected by the user, and “interesting fields” that are automatically selected by the system based on pre-specified criteria.

1.7 Acceleration Techniques

The above-described system provides significant flexibility by enabling a user to analyze massive quantities of minimally processed performance data “on the fly” at search time instead of storing pre-specified portions of the performance data in a database at ingestion time. This flexibility enables a user to see correlations in the performance data and perform subsequent queries to examine interesting aspects of the performance data that may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search time can involve a large amount of data and require a large number of computational operations, which can cause considerable delays while processing the queries. Fortunately, a number of acceleration techniques have been developed to speed up analysis operations performed at search time. These techniques include: (1) performing search operations in parallel by formulating a search as a map-reduce computation; (2) using a keyword index; (3) using a high performance analytics store; and (4) accelerating the process of generating reports. These techniques are described in more detail below.

1.7.1 Map-Reduce Technique

To facilitate faster query processing, a query can be structured as a map-reduce computation, wherein the “map” operations are delegated to the indexers, while the corresponding “reduce” operations are performed locally at the search head. For example, FIG. 5 illustrates how a search query 501 received from a client at search head 104 can split into two phases, including: (1) a “map phase” comprising subtasks 502 (e.g., data retrieval or simple filtering) that may be performed in parallel and are “mapped” to indexers 102 for execution, and (2) a “reduce phase” comprising a merging operation 503 to be executed by the search head when the results are ultimately collected from the indexers.

During operation, upon receiving search query 501, search head 104 modifies search query 501 by substituting “stats” with “prestats” to produce search query 502, and then distributes search query 502 to one or more distributed indexers, which are also referred to as “search peers.” Note that search queries may generally specify search criteria or operations to be performed on events that meet the search criteria. Search queries may also specify field names, as well as search criteria for the values in the fields or operations to be performed on the values in the fields. Moreover, the search head may distribute the full search query to the search peers as is illustrated in FIG. 3, or may alternatively distribute a modified version (e.g., a more restricted version) of the search query to the search peers. In this example, the indexers are responsible for producing the results and sending them to the search head. After the indexers return the results to the search head, the search head performs the merging operations 503 on the results. Note that by executing the computation in this way, the system effectively distributes the computational operations while minimizing data transfers.

1.7.2 Keyword Index

As described above with reference to the flow charts in FIGS. 2 and 3, event-processing system 100 can construct and maintain one or more keyword indices to facilitate rapidly identifying events containing specific keywords. This can greatly speed up the processing of queries involving specific keywords. As mentioned above, to build a keyword index, an indexer first identifies a set of keywords. Then, the indexer includes the identified keywords in an index, which associates each stored keyword with references to events containing that keyword, or to locations within events where that keyword is located. When an indexer subsequently receives a keyword-based query, the indexer can access the keyword index to quickly identify events containing the keyword.

1.7.3 High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 100 make use of a high performance analytics store, which is referred to as a “summarization table,” that contains entries for specific field-value pairs. Each of these entries keeps track of instances of a specific value in a specific field in the event data and includes references to events containing the specific value in the specific field. For example, an exemplary entry in a summarization table can keep track of occurrences of the value “94107” in a “ZIP code” field of a set of events, wherein the entry includes references to all of the events that contain the value “94107” in the ZIP code field. This enables the system to quickly process queries that seek to determine how many events have a particular value for a particular field, because the system can examine the entry in the summarization table to count instances of the specific value in the field without having to go through the individual events or do extractions at search time. Also, if the system needs to process all events that have a specific field-value combination, the system can use the references in the summarization table entry to directly access the events to extract further information without having to search all of the events to find the specific field-value combination at search time.

In some embodiments, the system maintains a separate summarization table for each of the above-described time-specific buckets that stores events for a specific time range, wherein a bucket-specific summarization table includes entries for specific field-value combinations that occur in events in the specific bucket. Alternatively, the system can maintain a separate summarization table for each indexer, wherein the indexer-specific summarization table only includes entries for the events in a data store that is managed by the specific indexer.

The summarization table can be populated by running a “collection query” that scans a set of events to find instances of a specific field-value combination, or alternatively instances of all field-value combinations for a specific field. A collection query can be initiated by a user, or can be scheduled to occur automatically at specific time intervals. A collection query can also be automatically launched in response to a query that asks for a specific field-value combination.

In some cases, the summarization tables may not cover all of the events that are relevant to a query. In this case, the system can use the summarization tables to obtain partial results for the events that are covered by summarization tables, but may also have to search other events that are not covered by the summarization tables to produce additional results. These additional results can then be combined with the partial results to produce a final set of results for the query. This summarization table and associated techniques are described in more detail in U.S. Pat. No. 8,682,925, issued on Mar. 25, 2014.

1.7.4 Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISE system can accelerate the process of periodically generating updated reports based on query results. To accelerate this process, a summarization engine automatically examines the query to determine whether generation of updated reports can be accelerated by creating intermediate summaries. (This is possible if results from preceding time periods can be computed separately and combined to generate an updated report. In some cases, it is not possible to combine such incremental results, for example where a value in the report depends on relationships between events from different time periods.) If reports can be accelerated, the summarization engine periodically generates a summary covering data obtained during a latest non-overlapping time period. For example, where the query seeks events meeting a specified criteria, a summary for the time period includes only events within the time period that meet the specified criteria. Similarly, if the query seeks statistics calculated from the events, such as the number of events that match the specified criteria, then the summary for the time period includes the number of events in the period that match the specified criteria.

In parallel with the creation of the summaries, the summarization engine schedules the periodic updating of the report associated with the query. During each scheduled report update, the query engine determines whether intermediate summaries have been generated covering portions of the time period covered by the report update. If so, then the report is generated based on the information contained in the summaries. Also, if additional event data has been received and has not yet been summarized, and is required to generate the complete report, the query can be run on this additional event data. Then, the results returned by this query on the additional event data, along with the partial results obtained from the intermediate summaries, can be combined to generate the updated report. This process is repeated each time the report is updated. Alternatively, if the system stores events in buckets covering specific time ranges, then the summaries can be generated on a bucket-by-bucket basis. Note that producing intermediate summaries can save the work involved in re-running the query for previous time periods, so only the newer event data needs to be processed while generating an updated report. These report acceleration techniques are described in more detail in U.S. Pat. No. 8,589,403, issued on Nov. 19, 2013, and U.S. Pat. No. 8,412,696, issued on Apr. 2, 2011.

1.8 Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards and visualizations that make it easy for developers to create applications to provide additional capabilities. One such application is the SPLUNK® APP FOR ENTERPRISE SECURITY, which performs monitoring and alerting operations and includes analytics to facilitate identifying both known and unknown security threats based on large volumes of data stored by the SPLUNK® ENTERPRISE system. This differs significantly from conventional Security Information and Event Management (SIEM) systems that lack the infrastructure to effectively store and analyze large volumes of security-related event data. Traditional SIEM systems typically use fixed schemas to extract data from pre-defined security-related fields at data ingestion time, wherein the extracted data is typically stored in a relational database. This data extraction process (and associated reduction in data size) that occurs at data ingestion time inevitably hampers future incident investigations, when all of the original data may be needed to determine the root cause of a security issue, or to detect the tiny fingerprints of an impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores large volumes of minimally processed security-related data at ingestion time for later retrieval and analysis at search time when a live security threat is being investigated. To facilitate this data retrieval process, the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemas for extracting relevant values from the different types of security-related event data, and also enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types of security-related information. In general, this security-related information can include any information that can be used to identify security threats. For example, the security-related information can include network-related information, such as IP addresses, domain names, asset identifiers, network traffic volume, uniform resource locator strings, and source addresses. (The process of detecting security threats for network-related information is further described in U.S. patent application Ser. No. 13/956,252, and U.S. patent application Ser. No. 13/956,262.) Security-related information can also include endpoint information, such as malware infection data and system configuration information, as well as access control information, such as login/logout information and access failure notifications. The security-related information can originate from various sources within a data center, such as hosts, virtual machines, storage devices and sensors. The security-related information can also originate from various sources in a network, such as routers, switches, email servers, proxy servers, gateways, firewalls and intrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitates detecting so-called “notable events” that are likely to indicate a security threat. These notable events can be detected in a number of ways: (1) an analyst can notice a correlation in the data and can manually identify a corresponding group of one or more events as “notable;” or (2) an analyst can define a “correlation search” specifying criteria for a notable event, and every time one or more events satisfy the criteria, the application can indicate that the one or more events are notable. An analyst can alternatively select a pre-defined correlation search provided by the application. Note that correlation searches can be run continuously or at regular intervals (e.g., every hour) to search for notable events. Upon detection, notable events can be stored in a dedicated “notable events index,” which can be subsequently accessed to generate various visualizations containing security-related information. Also, alerts can be generated to notify system operators when important notable events are discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizations to aid in discovering security threats, such as a “key indicators view” that enables a user to view security metrics of interest, such as counts of different types of notable events. For example, FIG. 7A illustrates an exemplary key indicators view 700 that comprises a dashboard, which can display a value 701, for various security-related metrics, such as malware infections 702. It can also display a change in a metric value 703, which indicates that the number of malware infections increased by 63 during the preceding interval. Key indicators view 700 additionally displays a histogram panel 704 that displays a histogram of notable events organized by urgency values, and a histogram of notable events organized by time intervals. This key indicators view is described in further detail in pending U.S. patent application Ser. No. 13/956,338 filed Jul. 31, 2013.

These visualizations can also include an “incident review dashboard” that enables a user to view and act on “notable events.” These notable events can include: (1) a single event of high importance, such as any activity from a known web attacker; or (2) multiple events that collectively warrant review, such as a large number of authentication failures on a host followed by a successful authentication. For example, FIG. 7B illustrates an exemplary incident review dashboard 710 that includes a set of incident attribute fields 711 that, for example, enables a user to specify a time range field 712 for the displayed events. It also includes a timeline 713 that graphically illustrates the number of incidents that occurred in one-hour time intervals over the selected time range. It additionally displays an events list 714 that enables a user to view a list of all of the notable events that match the criteria in the incident attributes fields 711. To facilitate identifying patterns among the notable events, each notable event can be associated with an urgency value (e.g., low, medium, high, critical), which is indicated in the incident review dashboard. The urgency value for a detected event can be determined based on the severity of the event and the priority of the system component associated with the event. The incident review dashboard is described further in “http://docs.splunk.com/Documentation/PCI/2.1.1/User/IncidentReviewdashboard.”

1.9 Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides various features that make it easy for developers to create various applications. One such application is the SPLUNK® APP FOR VMWARE®, which performs monitoring operations and includes analytics to facilitate diagnosing the root cause of performance problems in a data center based on large volumes of data stored by the SPLUNK® ENTERPRISE system.

This differs from conventional data-center-monitoring systems that lack the infrastructure to effectively store and analyze large volumes of performance information and log data obtained from the data center. In conventional data-center-monitoring systems, this performance data is typically pre-processed prior to being stored, for example by extracting pre-specified data items from the performance data and storing them in a database to facilitate subsequent retrieval and analysis at search time. However, the rest of the performance data is not saved and is essentially discarded during pre-processing. In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes of minimally processed performance information and log data at ingestion time for later retrieval and analysis at search time when a live performance issue is being investigated.

The SPLUNK® APP FOR VMWARE® can process many types of performance-related information. In general, this performance-related information can include any type of performance-related data and log data produced by virtual machines and host computer systems in a data center. In addition to data obtained from various log files, this performance-related information can include values for performance metrics obtained through an application programming interface (API) provided as part of the vSphere Hypervisor™ system distributed by VMware, Inc., of Palo Alto, Calif. For example, these performance metrics can include: (1) CPU-related performance metrics; (2) disk-related performance metrics; (3) memory-related performance metrics; (4) network-related performance metrics; (5) energy-usage statistics; (6) data-traffic-related performance metrics; (7) overall system availability performance metrics; (8) cluster-related performance metrics; and (9) virtual machine performance statistics. For more details about such performance metrics, please see U.S. patent Ser. No. 14/167,316 filed 29 Jan. 2014, which is hereby incorporated herein by reference. Also, see “vSphere Monitoring and Performance,” Update 1, vSphere 5.5, EN-001357-00, http://pubs.vmware.com/vsphere-55/topic/com.vmware.ICbase/PDF/vsphere-esxi-vcenter-server-551-monitoring-performance-guide.pdf.

To facilitate retrieving information of interest from performance data and log files, the SPLUNK® APP FOR VMWARE® provides pre-specified schemas for extracting relevant values from different types of performance-related event data, and also enables a user to define such schemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizations to facilitate detecting and diagnosing the root cause of performance problems. For example, one such visualization is a “proactive monitoring tree” that enables a user to easily view and understand relationships among various factors that affect the performance of a hierarchically structured computing system. This proactive monitoring tree enables a user to easily navigate the hierarchy by selectively expanding nodes representing various entities (e.g., virtual centers or computing clusters) to view performance information for lower-level nodes associated with lower-level entities (e.g., virtual machines or host systems). Exemplary node-expansion operations are illustrated in FIG. 7C, wherein nodes 733 and 734 are selectively expanded. Note that nodes 731-739 can be displayed using different patterns or colors to represent different performance states, such as a critical state, a warning state, a normal state or an unknown/offline state. The ease of navigation provided by selective expansion in combination with the associated performance-state information enables a user to quickly diagnose the root cause of a performance problem. The proactive monitoring tree is described in further detail in U.S. patent application Ser. No. 14/235,490 filed on 15 Apr. 2014, which is hereby incorporated herein by reference for all possible purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enables a user to select a specific time range and then view heterogeneous data, comprising events, log data and associated performance metrics, for the selected time range. For example, the screen illustrated in FIG. 7D displays a listing of recent “tasks and events” and a listing of recent “log entries” for a selected time range above a performance-metric graph for “average CPU core utilization” for the selected time range. Note that a user is able to operate pull-down menus 742 to selectively display different performance metric graphs for the selected time range. This enables the user to correlate trends in the performance-metric graph with corresponding event and log data to quickly determine the root cause of a performance problem. This user interface is described in more detail in U.S. patent application Ser. No. 14/167,316 filed on 29 Jan. 2014, which is hereby incorporated herein by reference for all possible purposes.

2.1 Managing Event Streams Generated from Captured Network Data

The disclosed embodiments provide a method and system for facilitating the processing of network data. As shown in FIG. 8, the network data may be captured using a data-processing system 800 in a distributed network environment. In the illustrated embodiment, system 800 includes a set of configuration servers 820 in communication with a set of remote capture agents 851-853 over one or more networks 890.

Although system 800 only depicts three configuration servers 820 and three remote capture agents 851-853, any number of configuration servers 820 and/or remote capture agents 851-853 may be configured to operate and/or communicate with one another within the data-processing system. For example, a single physical and/or virtual server may perform the functions of configuration servers 820. Alternatively, multiple physical and/or virtual servers or network elements may be logically connected to provide the functionality of configuration servers 820. The configuration server(s) may direct the activity of multiple distributed remote capture agents 851-853 installed on various client computing devices across one or more networks. In turn, remote capture agents 851-853 may be used to capture network data from multiple remote network data sources.

Further, embodiments described herein can be configured to capture network data in a cloud-based environment, such as cloud 840 depicted in the illustrated embodiment, and to generate events such as timestamped records of network activity from the network data. Remote capture agents 851-853 may capture network data originating from numerous distributed network servers, whether they are physical hardware servers or virtual machines running in cloud 840. In cloud-based implementations, remote capture agents 851-853 will generally only have access to information that is communicated to and received from machines running in the cloud-based environment. This is because, in a cloud environment, there is generally no access to any of the physical network infrastructure, as cloud computing may utilize a “hosted services” delivery model where the physical network infrastructure is typically managed by a third party.

Embodiments further include the capability to separate the data capture technology into a standalone component that can be installed directly on client servers, which may be physical servers or virtual machines residing on a cloud-based network (e.g., cloud 840), and used to capture and generate events for all network traffic that is transmitted in and out of the client servers. This eliminates the need to deploy and connect physical hardware to network TAPS or SPAN ports, thus allowing users to configure and change their data capture configuration on-the-fly rather than in fixed formats.

In the illustrated embodiment, remote capture agents 852-853 are in communication with network servers 830 residing in cloud 840, and remote capture agent 851 is located in cloud 840. Cloud 840 may represent any number of public and private clouds, and is not limited to any particular cloud configuration. Network servers 830 residing in cloud 840 may be physical servers and/or virtual machines in cloud 840, and network traffic to and from network servers 830 may be monitored by remote capture agent 851 and/or other remote capture agents connected to network servers 830. Further, remote capture agents 852-853 may also run in cloud 840 on physical servers and/or virtual machines. Those skilled in the art will appreciate that any number of remote capture agents may be included inside or outside of cloud 840.

Remote capture agents 851-853 may analyze network packets received from the networks(s) to which remote capture agents 851-853 are connected to obtain network data from the network packets and generate a number of events from the network data. For example, each remote capture agent 851-853 may listen for network traffic on network interfaces available to the remote capture agent. Network packets transmitted to and/or from the network interfaces may be intercepted by the remote capture agent and analyzed, and relevant network data from the network packets may be used by the remote capture agent to create events related to the network data. Such events may be generated by aggregating network data from multiple network packets, or each event may be generated using the contents of only one network packet. A sequence of events from a remote capture agent may then be included in one or more event streams that are provided to other components of system 800.

Configuration servers 820, data storage servers 835, and/or other network components may receive event data (e.g., event streams) from remote capture agents 851-853 and further process the event data before the event data is stored by data storage servers 835. In the illustrated embodiment, configuration servers 820 may transmit event data to data storage servers 835 over a network 801 such as a local area network (LAN), wide area network (WAN), personal area network (PAN), virtual private network, intranet, mobile phone network (e.g., a cellular network), Wi-Fi network, Ethernet network, and/or other type of network that enables communication among computing devices. The event data may be received over a network (e.g., network 801, network 890) at one or more event indexers (see FIG. 1) associated with data storage servers 835.

In addition, system 800 may include functionality to determine the types of network data collected and/or processed by each remote capture agent 851-853 to avoid data duplication at the indexers, data storage servers 835, and/or other components of system 800. For example, remote capture agents 852-853 may process network traffic from the same network. However, remote capture agent 852 may generate page view events from the network traffic, and remote capture agent 853 may generate request events (e.g., of HyperText Transfer Protocol (HTTP) requests and responses) from the network traffic.

In one or more embodiments, configuration servers 820 include configuration information that is used to configure the creation of events from network data on remote capture agents 851-853. In addition, such configuration may occur dynamically during event processing (e.g., at runtime). Conversely, because most conventional network capture technologies target specific end uses, they have been designed to operate in a fixed way and generally cannot be modified dynamically or easily to address different and changing business needs.

At least certain embodiments are adapted to provide a distributed remote capture platform in which the times at which events are communicated to the configuration servers 820 and the fields to be included in the events are controlled by way of user-modifiable configuration rather than by “hard coding” fixed events with pre-determined fields for a given network capture mechanism. The remote configuration capability also enables additional in-memory processing (e.g., filtering, transformation, normalization, aggregation, etc.) on events at the point of capture (e.g., remote capture agents 851-853) before the events are transmitted to other components of system 800.

Configuration information stored at each configuration server 820 may be created and/or updated manually at the configuration server and/or at a network element in communication with the configuration server. For example, a user may upload a configuration file containing configuration information for a remote capture agent to one or more configuration servers 820 for subsequent propagation to the remote capture agent. Alternatively, the user may use a GUI to provide the configuration information, as described in further detail below with respect to FIGS. 11A-11D. The configuration information may further be provided by one or more applications miming on a separate server or network element, such as data storage servers 835.

Remote capture agents 851-853 may then use the configuration information to generate events from captured network packets. When changes in the configuration information at the configuration server are detected at the remote capture agents, logic in the remote capture agents may be automatically reconfigured in response. This means the remote capture agents may be dynamically configured to produce different events, transform the events, and/or communicate event streams to different components of system 800. Dynamic configuration of the generation of events from captured network packets may also be performed by other components (e.g., configuration servers 820, data storage servers 835, etc.), in lieu of or in addition to the remote capture agents.

To detect changes in configuration information at configuration servers 820, remote capture agents 851-853 may poll configuration servers 820 at periodic intervals for updates to the configuration information. The updates may then be pulled from configuration servers 820 by remote capture agents 851-853. Conversely, updates to the configuration information may be pushed from configuration servers 820 to remote capture agents 851-853 at periodic intervals and/or when changes to the configuration information have been made.

In one embodiment, configuration servers 820 include a list of event streams generated by remote capture agents 851-853, as well as the configuration information used to generate the event streams at remote capture agents 851-853. The configuration information may include a unique identifier for each event stream, the types of events to be included in the event stream, one or more fields to be included in each event, and/or one or more filtering rules for filtering events to be included in the event stream. Using configuration information to dynamically modify network data capture by remote capture agents (e.g., remote capture agents 851-853) is described in a co-pending non-provisional application by inventor Michael Dickey, entitled “Distributed Processing of Network Data Using Remote Capture Agents,” having Ser. No. 14/253,783, and filing date 15 Apr. 2014, which is incorporated herein by reference.

In one or more embodiments, system 800 includes functionality to perform protocol-based capture and analysis of network data using remote capture agents 851-853. First, remote capture agents 851-853 may be configured to generate event streams from packet flows captured at remote capture agents 851-853 based on protocol classifications for the packet flows. Second, configuration servers 820 may include functionality to streamline the configuration of remote capture agents 851-853 in generating protocol-specific event streams. Third, configuration servers 820 and/or remote capture agents 851-853 may enable the use of capture triggers to capture additional network data based on the identification of potential security risks from previously generated event streams. Protocol-based capture and analysis of network data using remote capture agents is described in a co-pending non-provisional application by inventors Vladimir Shcherbakov and Michael Dickey and filed on the same day as the instant application, entitled “Protocol-Based Capture of Network Data Using Remote Capture Agents,” having Ser. No. 14/528,898, and filing date 30 Oct. 2014, which is incorporated herein by reference.

FIG. 9 shows a remote capture agent 950 in accordance with the disclosed embodiments. In the illustrated embodiment, remote capture agent 950 is adapted to receive configuration information from one or more configuration servers 820 over network 801. Remote capture agent 950 may be installed at a customer's premises on one or more of the customer's computing resources. Remote capture agent 950 may also be installed in a remote computing environment such as a cloud computing system. For example, remote capture agent 950 may be installed on a physical server and/or in a virtual computing environment (e.g., virtual machine) that is distributed across one or more physical machines.

Remote capture agent 950 includes a communications component 903 configured to communicate with network elements on one or more networks (e.g., network 801) and send and receive network data (e.g., network packets) over the network(s). As depicted, communications component 903 may communicate with configuration servers 820 over network 801. Communications component 903 may also communicate with one or more sources of network data, such as network servers 830 of FIG. 8.

Network data received at communications component 903 may be captured by a capture component 905 coupled with communications component 903. Capture component 905 may capture some or all network data from communications component 903. For example, capture component 905 may capture network data based on the sources and/or destinations of the network data, the types of the network data, the protocol associated with the network data, and/or other characteristics of the network data.

In addition, the network data may be captured based on configuration information stored in a configuration component 904 of remote capture agent 950. As mentioned above, the configuration information may be received from configuration servers 820 over network 801. The configuration information may then be used to dynamically configure or reconfigure remote capture agent 950 in real-time. For example, newly received configuration information in configuration component 904 may be used to configure the operation of remote capture agent 950 during processing of events from network data by remote capture agent 950.

To dynamically configure remote capture agent 950, configuration information received by configuration component 904 from configuration servers 820 may be provided to other components of remote capture agent 950. More specifically, remote capture agent 950 includes an events generator 907 that receives network data from network data capture component 905 and generates events from the network data based on configuration information from configuration component 904.

Using configuration information provided by configuration servers 820, remote capture agent 950 can be instructed to perform any number of event-based processing operations. For example, the configuration information may specify the generation of event streams associated with network (e.g., HTTP, Simple Mail Transfer Protocol (SMTP), Domain Name System (DNS)) transactions, business transactions, errors, alerts, clickstream events, and/or other types of events. The configuration information may also describe custom fields to be included in the events, such as values associated with specific clickstream terms. The configuration information may include additional parameters related to the generation of event data, such as an interval between consecutive events and/or the inclusion of transactions and/or errors matching a given event in event data for the event. Configuration information for configuring the generation of event streams from network data captured by remote capture agents is further described in the above-referenced applications.

An events transformer 909 may further use the configuration information to transform some or all of the network data from capture component 905 and/or events from events generator 907 into one or more sets of transformed events. In one or more embodiments, transformations performed by events transformer 909 include aggregating, filtering, cleaning, and/or otherwise processing events from events generator 907. Configuration information for the transformations may thus include a number of parameters that specify the types of transformations to be performed, the types of data on which the transformations are to be performed, and/or the formatting of the transformed data.

A rules comparison engine 908 in remote capture agent 950 may receive events from events generator 907 and compare one or more fields from the events to a set of filtering rules in the configuration information to determine whether to include the events in an event stream. For example, the configuration information may specify packet-level, protocol-level, and/or application-level filtering of event data from event streams generated by remote capture agent 950.

Finally, a data enrichment component 911 may further transform event data into a different form or format based on the configuration information from configuration component 904. For example, data enrichment component 911 may use the configuration information to normalize the data so that multiple representations of the same value (e.g., timestamps, measurements, etc.) are converted into the same value in transformed event data.

Data can be transformed by data enrichment component 911 in any number of ways. For example, remote capture agent 950 may reside on a client server in Cupertino, Calif., where all the laptops associated with the client server have been registered with the hostname of the client server. Remote capture agent 950 may use the registration data to look up an Internet Protocol (IP) address in a look-up table (LUT) that is associated with one or more network elements of the client server's local network. Remote capture agent 950 may then resolve a user's IP address into the name of the user's laptop, thereby enabling inclusion of the user's laptop name in transformed event data associated with the IP address. The transformed event data may then be communicated to configuration servers 820 and/or a central transformation server residing in San Francisco for further processing, indexing, and/or storage.

As mentioned above, remote capture agent 950 may perform protocol-based generation of event streams from network data. As shown in FIG. 9B, configuration component 904 may obtain protocol-specific configuration information (e.g., protocol-specific configuration information A 912, protocol-specific configuration information B 914) from one or more configuration servers (e.g., configuration servers 820). For example, configuration information from the configuration server(s) may be transmitted over network 801 to communications component 903, which provides the configuration information to configuration component 904 for storage and/or further processing.

Protocol-specific configuration information from configuration component 904 may be used to configure the generation of event streams (e.g., event stream C 932, event stream D 934, event stream E 940, event stream F 942) based on protocol classifications of network packets (e.g., network packets C 916, network packets D 918) captured by capture component 905. For example, protocol-specific configuration information from configuration component 904 may specify the creation of event streams from the network packets based on the protocols used in the network packets, such as HTTP, DNS, SMTP, File Transfer Protocol (FTP), Server Message Block (SMB), Network File System (NFS), Internet Control Message Protocol (ICMP), email protocols, database protocols, and/or security protocols. Such event streams may include event attributes that are of interest to the respective protocols.

Before the event streams are generated from the network packets, capture component 905 may assemble the network packets into one or more packets flows (e.g., packet flow C 920, packet flow D 922). First, capture component 905 may identify the network packets in a given packet flow based on control information in the network packets. The packet flow may represent a communication path between a source and a destination (e.g., host, multicast group, broadcast domain, etc.) on the network. As a result, capture component 905 may identify network packets in the packet flow by examining network (e.g., IP) addresses, ports, sources, destinations, and/or transport protocols (e.g., Transmission Control Protocol (TCP), User Datagram Protocol (UDP), etc.) from the headers of the network packets.

Next, capture component 905 may assemble the packet flow from the network packets. For example, capture component 905 may assemble a TCP packet flow by rearranging out-of-order TCP packets. Conversely, capture component 905 may omit reordering of the network packets in the packet flow if the network packets use UDP and/or another protocol that does not provide for ordered packet transmission.

After the packet flow is assembled, capture component 905 and/or another component of remote capture agent 950 may detect encryption of the network packets in the packet flow by analyzing the byte signatures of the network packets' payloads. For example, the component may analyze the network packets' payloads for byte signatures that are indicative of Secure Sockets Layer (SSL) and/or Transport Layer Security (TLS) encryption. If the network packets are detected as encrypted, the component may decrypt the network packets. For example, the component may have access to private keys from an SSL server used by the network flow and perform decryption of the network packets to obtain plaintext payload data in the order in which the data was sent. Such access to private keys may be given to remote capture agent 950 by an administrator associated with the network flow, such as an administrator of the host from which the network packets are transmitted.

Events generator 907 may then obtain a protocol classification (e.g., protocol classification C 924, protocol classification D 926) for each packet flow identified, assembled, and/or decrypted by capture component 905. For example, events generator 907 may use a protocol-decoding mechanism to analyze the headers and/or payloads of the network packets in the packet flow and return protocol identifiers of one or more protocols used in the network packets. The protocol-decoding mechanism may additionally provide metadata related to the protocols, such as metadata related to traffic volume, application usage, application performance, user and/or host identifiers, content (e.g., media, files, etc.), and/or file metadata (e.g., video codecs and bit rates).

Once the protocol classification is obtained for a packet flow, events generator 907 may use protocol-specific configuration information associated with the protocol classification from configuration component 904 to build an event stream (e.g., event stream C 932, event stream D 934) from the packet flow. As mentioned above and in the above-referenced application, the event stream may include time-series event data generated from network packets in the packet flow. To create the event stream, events generator 907 may obtain one or more event attributes associated with the protocol classification from the configuration information. Next, event generator 907 may extract the event attribute(s) from the network packets in the first packet flow. Events generator 907 may then include the extracted event attribute(s) in the event stream.

For example, events generator 907 may obtain a protocol classification of DNS for a packet flow from capture component 905 and protocol-specific configuration information for generating event streams from DNS traffic from configuration component 904. The protocol-specific configuration information may specify the collection of event attributes such as the number of bytes transferred between the source and destination, network addresses and/or identifiers for the source and destination, DNS message type, DNS query type, return message, response time to a DNS request, DNS transaction identifier, and/or a transport layer protocol. In turn, events generator 907 may parse the protocol-specific configuration to identify the event attributes to be captured from the packet flow. Next, events generator 907 may extract the specified event attributes from the network packets in the packet flow and/or metadata received with the protocol classification of the packet flow and generate time-stamped event data from the extracted event attributes. Events generator 907 may then provide the time-stamped event data in an event stream to communications component 903 for transmission of the event stream over a network to one or more configuration servers, data storage servers, indexers, and/or other components for subsequent storage and processing of the event stream by the component(s).

As described above and in the above-referenced application, network data from capture component 905 and/or event data from events generator 907 may be transformed by events transformer 909 into transformed event data that is provided in lieu of or in addition to event data generated by events generator 907. For example, events transformer 909 may aggregate, filter, clean, and/or otherwise process event attributes from events generator 907 to produce one or more sets of transformed event attributes (e.g., transformed event attributes 1 936, transformed event attributes z 938). Events transformer 909 may then include the transformed event attributes into one or more additional event streams (e.g., event stream 1 940, event stream z 942) that may be transmitted over the network for subsequent storage and processing of the event stream(s) by other components on the network. Such transformation of event data at remote capture agent 950 may offload subsequent processing of the event data at configuration servers and/or other components on the network. Moreover, if the transformation reduces the size of the event data (e.g., by aggregating the event data), network traffic between remote capture agent 950 and the other components may be reduced, along with the storage requirements associated with storing the event data at the other components.

As with protocol-based generation of event data by events generator 907, events transformer 909 may use protocol-specific configuration information from configuration component 904 to transform network and/or event data from a given packet flow and/or event stream. For example, events transformer 909 may obtain protocol-specific configuration information for aggregating HTTP events and use the configuration information to generate aggregated HTTP events from HTTP events produced by events generator 907. The configuration information may include one or more key attributes used to generate a unique key representing an aggregated event from the configuration information. For example, key attributes for generating an aggregated HTTP event may include the source and destination IP addresses and ports in a set of HTTP events. A different unique key and aggregated HTTP event may thus be generated for each unique combination of source and destination IP addresses and ports in the HTTP events.

The configuration information may also specify one or more aggregation attributes to be aggregated prior to inclusion in the aggregated event. For example, aggregation attributes for generating an aggregated HTTP event from HTTP event data may include the number of bytes and packets sent in each direction between the source and destination. Data represented by the aggregation attributes may be included in the aggregated HTTP event by summing, averaging, and/or calculating a summary statistic from the number of bytes and packets sent in each direction between the source and destination. Aggregation of event data is described in further detail below with respect to FIG. 11C.

FIG. 10 shows a configuration server 1020 in accordance with the disclosed embodiments. As shown in the illustrated embodiment, configuration server 1020 is in communication with multiple remote capture agents 1050 over network 890, and remote capture agents 1050 are distributed throughout network 890 and cloud 840. Configuration server 1020 includes a communications component 1010 that receives events from remote capture agents 1050 over network 890 and/or from cloud 840. Communications component 1010 may also communicate with one or more data storage servers, such as data storage servers 835 of FIG. 8.

Configuration server 1020 also includes a configuration component 1004 that stores configuration information for remote capture agents 1050. As described above, the configuration information may specify the types of events to produce, data to be included in the events, and/or transformations to be applied to the data and/or events to produce transformed events. Some or all of the transformations may be specified in a set of filtering rules 1021 that may be applied to event data at remote capture agents 1050 to determine a subset of the event data to be included in one or more event streams that are sent to configuration server 1020 and/or other components.

Configuration information from configuration component 1004 may also be used to manage an event stream lifecycle of the event streams. The event stream lifecycle may be a permanent event stream lifecycle, in which generation of events in an event stream continues after the event stream's creation until the event stream is manually disabled, deleted, or otherwise inactivated. Conversely, the event stream lifecycle may be an ephemeral event stream lifecycle, in which events in an event stream are generated on a temporary basis, and the event stream has an end time at which the event stream is terminated. For example, an ephemeral event stream may be created by a capture trigger for generating additional time-series event data from the network packets on remote capture agents 1050 based on a security risk, as described above and in the above-referenced applications.

To distinguish between permanent and ephemeral event streams, the configuration information may include a parameter that identifies each event stream as “permanent” or “ephemeral.” The configuration information may also include attributes such as a start time and end time for each ephemeral event stream. Remote capture agents 1050 may begin generating time-series event data for the ephemeral event stream at the start time and terminate the ephemeral event stream at the end time.

Alternatively, the creation and termination of an ephemeral event stream may be managed by configuration component 1004 instead of remote capture agents 1050. For example, configuration component 1004 may track the start and end times of an ephemeral event stream. At the start time of the ephemeral event stream, configuration component 1004 may provide remote capture agents 1050 with configuration information for the ephemeral event stream. At the end time of the ephemeral end stream, configuration component 1004 may remove all references to the ephemeral event stream from the configuration information and transmit the configuration information to remote capture agents 1050. Because configuration component 1004 uses updates to the configuration information to create and terminate an ephemeral event stream, remote capture agents 1050 may not be required to distinguish between ephemeral event streams and permanent event streams.

Configuration server 1020 may also include a data processing component 1011 that performs additional processing of the event streams based on configuration information from configuration component 1004. As discussed in the above example with respect to FIGS. 9A-9B, event data may be transformed at a remote capture agent (e.g., remote capture agent 950) during resolution of the user's IP address into the name of the user's laptop. The transformed event data may be sent to configuration server 1020 and/or a transformation server for additional processing and/or transformation, such as taking the host name from the transformed event data, using an additional LUT to obtain a user identifier (user ID) of the person to which the laptop is registered, and further transforming the event data by including the user ID in the event data before forwarding the event data to a third server (e.g., a transformation server) for another round of processing.

In one or more embodiments, configuration server 1020 and remote capture agents 1050 include functionality to improve the management of event streams generated from captured network data, including event streams associated with various protocols, applications, and event stream lifecycles. As shown in FIG. 10, configuration server 1020 may provide a GUI 1025 that can be used to configure or reconfigure the information contained in configuration component 1004. The configuration information from configuration component 1004 may then be propagated to remote capture agents 1050 and used by remote capture agents 1050 to generate time-series event data from network packets captured by remote capture agents 1050.

GUI 1025 may include a number of features and/or mechanisms for facilitating the management of multiple event streams. First, GUI 1025 may group the event streams by one or more event stream attributes associated with the event streams. For example, GUI 1025 may allow a user to specify an event stream attribute such as a category of an event stream, a protocol used by network packets from which the event stream is generated, an application used to create the event stream, and/or an event stream lifecycle of the event stream. GUI 1025 may display event stream information for one or more subsets of the event streams represented by the grouping of the event streams by the specified event stream attribute. GUI 1025 may also group the event stream information by multiple event stream attributes. For example, GUI 1025 may group the event stream information by a first event stream attribute such as application, category, or protocol. GUI 1025 may then apply a second grouping of the event stream information by permanent or ephemeral event stream lifecycles. As a result, a user may view permanent event streams associated with a given category, application, or protocol separately from ephemeral event streams associated with the category, application or protocol. Grouping and managing event streams is described in further detail below with respect to FIGS. 17A-17C.

Second, GUI 1025 may display, along with the grouped event stream information, graphs of metrics associated with time-series event data in the event streams. For example, GUI 1025 may include a sparkline of network traffic over time for each event stream under a given grouping of event streams. GUI 1025 may also show a sparkline of aggregate network traffic and/or another aggregated metric for all event streams listed under the grouping. In-line visualizations of metrics related to event streams and/or captured network data is described in further detail below with respect to FIGS. 17A-17B.

Third, GUI 1025 may enable the management of ephemeral event streams. For example, GUI 1025 may allow a user to create a new ephemeral event stream, disable an existing event stream, delete an existing event stream, and/or modify an end time for terminating an existing event stream. Managing ephemeral event streams generated from captured network data is described in further detail below with respect to FIG. 17C.

Finally, GUI 1025 may provide bidirectional linking of ephemeral event streams to creators of the ephemeral event streams. For example, GUI 1025 may include a hyperlink from event stream information for an ephemeral event stream to creation information for a creator of the ephemeral event stream, such as an application and/or capture trigger used to create the ephemeral event stream. GUI 1025 may also include another hyperlink from the creation information to the event stream information to facilitate understanding and analysis related to the context under which the ephemeral event stream was generated. Bidirectional linking of ephemeral event streams to creators of the ephemeral event streams is described in further detail below with respect to FIGS. 17C-17D.

Finally, configuration server 1020 may provide a risk-identification mechanism 1007 for identifying a security risk from time-series event data generated by remote capture agents 1050, as well as a capture trigger 1009 for generating additional time-series event data based on the security risk. For example, risk-identification mechanism 1007 may allow a user to view and/or search for events that may represent security risks through GUI 1025. Risk-identification mechanism 1007 and/or GUI 1025 may also allow the user to set and/or activate capture trigger 1009 based on the events shown and/or found through risk-identification mechanism 1007 and/or GUI 1025.

In particular, risk-identification mechanism 1007 and/or GUI 1025 may allow the user to manually activate capture trigger 1009 after discovering a potential security risk. In turn, the activated capture trigger 1009 may modify configuration information in configuration component 1004 that is propagated to remote capture agents 1050 to trigger the capture of additional network data by remote capture agents 1050.

Alternatively, risk-identification mechanism 1007 may allow the user to create a search and/or recurring search for time-series event data that may match a security risk. If the search and/or recurring search finds time-series event data that matches the security risk, capture trigger 1009 may automatically be activated to enable the generation of additional time-series event data, such as event data containing one or more attributes associated with one or more protocols that facilitate analysis of the security risk. Such automatic activation of capture trigger 1009 may allow the additional event data to be generated immediately after a notable event is detected, thus averting the loss of captured network data that results from enabling additional network data capture only after a potential security risk is manually identified (e.g., by an analyst). Triggering the generation of additional time-series event data from network packets on remote agents based on potential security risks is described in further detail below with respect to FIGS. 12A-12B.

FIG. 11A shows an exemplary screenshot in accordance with the disclosed embodiments. More specifically, FIG. 11A shows a screenshot of a GUI, such as GUI 1025 of FIG. 10. As described above, the GUI may be used to obtain configuration information that is used to configure the generation of event streams containing time-series event data at one or more remote capture agents distributed across a network.

As shown in FIG. 11A, the GUI includes a table with a set of columns 1102-1108 containing high-level information related to event streams that may be created using the configuration information. Each row of the table may represent an event stream, and rows of the table may be sorted by column 1102.

Column 1102 shows an alphabetized or otherwise ordered or unordered list of names of the event streams, and column 1104 provides descriptions of the event streams. For example, columns 1102-1104 may include names and descriptions of event streams generated from HTTP, Dynamic Host Configuration Protocol (DHCP), DNS, FTP, email protocols, database protocols, NFS, Secure Message Block (SMB), security protocols, Session Initiation Protocol (SIP), TCP, and/or UDP network traffic. Columns 1102-1104 may thus indicate that event streams may be generated based on transport layer protocols, session layer protocols, presentation layer protocols, and/or application layer protocols.

A user may select a name of an event stream under column 1102 to access and/or update configuration information for configuring the generation of the event stream. For example, the user may select “DemoHTTP” in column 1102 to navigate to a screen of the GUI that allows the user to specify event attributes, filters, and/or aggregation information related to creating the “DemoHTTP” event stream, as discussed in further detail below with respect to FIGS. 11B-11E.

Column 1106 specifies whether each event stream is enabled or disabled. For example, column 1106 may indicate that the “AggregateHTTP,” “DemoHTTP,” “dns,” “ftp,” “mysql-query,” “sip,” “tcp,” and “udp” event streams are enabled. If an event stream is enabled, time-series event data may be included in the event stream based on the configuration information for the event stream.

Column 1108 specifies whether each event stream is cloned from an existing event stream. For example, column 1108 may indicate that the “AggregateHTTP” and “DemoHTTP” event streams have been cloned (e.g., copied) from other event streams, while the remaining event streams may be predefined with default event attributes.

The GUI also includes a user-interface element 1110 (e.g., “Clone Stream”). A user may select user-interface element 1110 to create a new event stream as a copy of an event stream listed in the GUI. After user-interface element 1110 is selected, an overlay may be displayed that allows the user to specify a name for the new event stream, a description of the new event stream, and an existing event stream from which the new event stream is to be cloned. The new event stream may then be created with the same event attributes and/or configuration options as the existing event stream, and the user may use the GUI to customize the new event stream as a variant of the existing event stream (e.g., by adding or removing event attributes, filters, and/or aggregation information).

FIG. 11B shows an exemplary screenshot in accordance with the disclosed embodiments. More specifically, FIG. 11B shows a screenshot of the GUI of FIG. 11A after the user has selected “DemoHTTP” from column 1102. In response to the selection, the GUI displays configuration information and/or configuration options for the “DemoHTTP” event stream.

Like the GUI of FIG. 11A, the GUI of FIG. 11B may include a table. Each row in the table may represent an event attribute that is eligible for inclusion in the event stream. For example, an event attribute may be included in the table if the event attribute can be obtained from network packets that include the protocol of the event stream. Columns 1112-1120 of the table may allow the user to use the event attributes to generate time-series event data that is included the event stream. First, column 1112 includes a series of checkboxes that allows the user to include individual event attributes in the event stream or exclude the event attributes from the event stream. If a checkbox is checked, the corresponding event attribute is added to the event stream, and the row representing the event attribute is shown with other included event attributes in an alphabetized list at the top of the table. If a checkbox is not checked, the corresponding event attribute is omitted from the event stream, and the row representing the event attribute is shown with other excluded event attributes in an alphabetized list following the list of included event attributes. Those skilled in the art will appreciate that the GUI may utilize other sortings and/or rankings of event attributes in columns 1112-1120.

Columns 1114-1118 may provide information related to the event attributes. Column 1114 may show the names of the event attributes, column 1116 may provide a description of each event attribute, and column 1118 may provide a term representing the event attribute. In other words, columns 1114-1118 may allow the user to identify the event attributes and decide whether the event attributes should be included in the event stream.

Column 1120 may include a series of links labeled “Add.” The user may select one of the links to access a portion of the GUI that allows the user to set a filter for the corresponding event attribute. The filter may then be used in the generation of the event stream from network data. Creation of filters for generating event streams from network packets is described in further detail below with respect to FIGS. 11D-11E.

The GUI of FIG. 11B also includes information 1122 related to the event stream. For example, information 1122 may include the name (e.g., “DemoHTTP”) of the event stream, the protocol classification and/or type (e.g., “http.event”) of the event stream, and the number of filters (e.g., “0 filters configured”) set for the event stream. Information 1122 may also include a checkbox 1136 that identifies if the event stream contains aggregated event data. If checkbox 1136 is checked, the GUI may be updated with options associated with configuring the generation of an aggregated event stream, as described below with respect to FIG. 11C.

Finally, the GUI of FIG. 11B includes a set of user-interface elements 1124-1134 for managing the event stream. First, the user may select user-interface element 1124 (e.g., “Enabled”) to enable generation of the event stream from network data and user-interface element 1126 (e.g., “Disabled”) to disable the generation of the event stream from the network data.

Next, the user may select user-interface element 1128 (e.g., “Clone”) to clone the event stream and user-interface element 1130 (e.g., “Delete”) to delete the event stream. If the user selects user-interface element 1128, the GUI may obtain a name and description for the cloned event stream from the user. Next, the GUI may copy the content of columns 1112-1120, including configuration options (e.g., checkboxes in column 1112 and filters added using links in column 1120) that have been changed but not yet saved by the user, to a new screen for configuring the generation of the cloned event stream.

If the user selects user-interface element 1130, the GUI may remove the event stream from the table in FIG. 11A. In turn, a representation of the event stream may be removed from the configuration information to stop the generation of time-series event data in the event stream by one or more remote capture agents.

The user may select user-interface element 1132 (e.g., “Cancel”) to discharge changes to the configuration information made in the current screen of the GUI. Conversely, the user may select user-interface 1134 (e.g., “Save”) to propagate the changes to the configuration information, and in turn, update the generation of event data from network packets captured by the remote capture agents based on the changes.

FIG. 11C shows an exemplary screenshot in accordance with the disclosed embodiments. In particular, FIG. 11C shows a screenshot of the GUI of FIG. 11B after checkbox 1136 has been checked. Because checkbox 1136 is checked, the GUI includes a number of user-interface elements for configuring the generation of an aggregated event stream. The aggregated event stream may include aggregated event data, which in turn may be generated by aggregating and/or extracting event attributes from one or more network packets in a packet flow. For example, an HTTP event may be generated from one to several HTTP packets representing an HTTP request/response pair. Event attributes from multiple HTTP events may then be aggregated into a single aggregated HTTP event to reduce the amount of event data generated from the network data without losing important attributes of the event data.

As shown in FIG. 11C, a new column 1138 is added to the table. Each row in column 1138 may include a pair of user-interface elements (e.g., buttons) that allow the user to identify the corresponding event attribute as a key attribute or an aggregation attribute. One or more key attributes may be used to generate a unique key representing each aggregated event, and one or more aggregation attributes may be aggregated prior to inclusion in the aggregated event. Some event attributes (e.g., “dest_ip,” “src_ip,” “uri_path”) may only be used as key attributes because the event attributes are not numeric in nature. On the other hand, event attributes that may be summed (e.g., “dest_port,” “status,” “bytes,” “bytes_in,” “bytes_out,” “time taken”) may have numeric values.

Event attributes identified as key attributes in column 1138 may be sorted at the top of the table, followed by event attributes identified as aggregation attributes. Event attributes that are not included in the event stream (e.g., event attributes with unchecked checkboxes in column 1112) may be shown below the aggregation attributes in the table. Alternatively, event attributes may be displayed in the table according to other sortings and/or rankings.

While sums are the only type of aggregation shown in the GUI of FIG. 11C, other types of aggregation may also be used to generate aggregated event data. For example, aggregated event streams may be created using minimums, maximums, averages, standard deviations, and/or other summary statistics of event attributes.

The GUI of FIG. 11C also includes a user-interface element 1140 (e.g., a text box) for obtaining an aggregation interval over which event attributes are to be aggregated into a single aggregated event. The aggregation interval may be increased to increase the amount of aggregation in the aggregated event stream and reduced to decrease the amount of aggregation in the aggregated event stream.

For example, column 1138 may indicate that the “dest_p,” “dest_port,” “src_ip,” “status,” and “uri_path” event attributes are specified as key attributes and the “bytes,” “bytes_in,” “bytes_out,” and “time taken” event attributes are specified as aggregation attributes. Similarly, an aggregation interval of 60 seconds may be obtained from user-interface element 1140. As a result, the aggregated event stream may include aggregated events generated from event data over a 60-second interval. After each 60-second interval has passed, a separate aggregated event with a unique key may be generated for each unique combination of “dest_ip,” “dest_port,” “src_ip,” “status,” and “uri_path” key attributes encountered during the interval. Values of “bytes,” “bytes_in,” “bytes_out,” and “time taken” for events within the interval that match the unique combination of key attributes may also be summed and/or otherwise aggregated into the aggregated event. Aggregated events generated from the configuration options may then be shown in the same GUI, as described in further detail below with respect to FIG. 11F.

Such configuration of event streams and/or aggregated event streams may allow network data to be captured at different levels of granularity and/or for different purposes. For example, an aggregated event stream may include all possible event attributes for the event stream to enable overall monitoring of network traffic. On the other hand, one or more unaggregated event streams may be created to capture specific types of network data at higher granularities than the aggregated event stream. In addition, multiple event streams may be created from the same packet flow and/or event data to provide multiple “views” of the packet flow and/or event data.

FIG. 11D shows an exemplary screenshot in accordance with the disclosed embodiments. More specifically, FIG. 11D shows a screenshot of the GUI of FIGS. 11B-11C after an “Add” link in column 1120 is selected. For example, the GUI of FIG. 11D may be shown as an overlay on the screens of FIGS. 11B-11C to enable the addition of filters to configuration information for the event stream(s) and/or aggregated event stream(s) shown on the screens.

As with the screenshots of FIGS. 11A-11C, the GUI of FIG. 11D includes information and/or user-interface elements organized into a table. Rows of the table may represent filters for an event stream and/or aggregated event stream, and columns 1142-1150 of the table may facilitate identification and/or configuration of the filters.

First, column 1142 may provide a list of terms representing event attributes to which the filters are to be applied. For example, column 1142 may specify an “http.status” term representing the “status” event attribute and an “http.uri-stem” term representing the “uri_path” event attribute.

Column 1144 may be used to provide a comparison associated with each filter. For example, a user may select a cell under column 1144 to access a drop-down menu of possible comparisons for the corresponding filter. As shown in FIG. 11D, the second cell of column 1144 is selected to reveal a drop-down menu of comparisons for a string-based event attribute (e.g., “uri_path”). Within the drop-down menu, “Regular Expression” is selected, while other options for the comparison may include “False,” “True,” “Is defined,” “Is not defined,” “Not Regular Expression,” “Exactly matches,” “Does not exactly match,” “Contains,” “Does not contain,” “Starts with,” “Does not start with,” “Ends with,” “Does not end with,” “Ordered before,” “Not ordered before,” “Ordered after,” and “Not ordered after.” As a result, a number of comparisons may be made with string-based event attributes during filtering of network data by the string-based event attributes.

Column 1146 may allow the user to specify a value against which the comparison in column 1144 is made. Cells in column 1146 may be text-editable fields and/or other user-interface elements that accept user input. For example, the second cell of column 1146 may include a value of “admin” that is entered by the user. Consequently, the values in the second cells of columns 1144-1146 may be used to generate a filter that determines if the “uri_path” event attribute from network data matches a regular expression of “admin.” If the network data matches the regular expression, the network data may be used to generate event data, which may subsequently be used to generate aggregated event data. If the network data does not match the regular expression, generation of event data from the network data may be omitted.

Column 1148 may include a set of checkboxes with a “Match All” header. The user may check a checkbox in column 1148 to require each value in a multi-value event attribute to match the filter. For example, the user may check a checkbox in column 1148 for a filter that is applied to a checksum event attribute to ensure that each of multiple checksums in a given network packet and/or event satisfies the comparison in the filter.

Column 1150 may allow the user to delete filters from the configuration information. For example, the user may select a user-interface (e.g., an icon) in a cell of column 1150 to remove the corresponding filter from the configuration information.

The GUI also includes a set of user-interface elements 1152-1154 for determining the applicability of individual filters or all filters to the network data. For example, the user may select user-interface element 1152 (e.g., “All”) to apply the filters so that only data that matches all filters in the table is used to generate events. Conversely, the user may select user-interface element 1154 (e.g., “Any”) to apply the filters so that data matching any of the filters in the data is used to generate events. In other words, user-interface element 1152 may be selected to apply a logical conjunction to the filters, while user-interface element 1154 may be selected to apply a logical disjunction to the filters.

FIG. 11E shows an exemplary screenshot in accordance with the disclosed embodiments. As with the screenshot of FIG. 11D, FIG. 11E shows a GUI for adding and/or managing filters for generating event data at one or more remote capture components.

Within the GUI of FIG. 11E, the first cell of column 1144 is selected. In turn, a drop-down menu of possible comparisons is shown for the corresponding filter. Because the filter relates to a numeric event attribute (e.g., an HTTP status code), comparisons in column 1144 may be numeric in nature. For example, the “Greater than” comparison is selected, while other possible comparisons may include “False,” “True,” “Is defined,” “Is not defined,” “Equals,” “Does not equal,” “Less than,” “Greater than or equal to,” and “Less than or equal to.” The differences in comparisons shown in FIG. 11E and FIG. 11D may ensure that comparisons that are meaningful and/or relevant to the types of event attributes specified in the filters are used with the filters.

FIG. 11F shows an exemplary screenshot in accordance with the disclosed embodiments. More specifically, FIG. 11F shows a screenshot of a GUI, such as GUI 1025 of FIG. 10. The GUI of FIG. 11F may provide information related to aggregated events, such as aggregated events generated using the GUI of FIG. 11C.

As shown in FIG. 11F, a first column 1156 contains a timestamp of an aggregated event, and a second column 1158 shows the aggregated event. Within column 1158, the aggregated event includes a number of event attributes. Some of the event attributes (e.g., “dest_ip,” “dest_port,” “src_ip,” “status,” “uri_path”) are key attributes that are used to uniquely identify the aggregated event, and other event attributes (e.g., “dest_port,” “status,” “bytes,” “bytes_in,” “bytes_out,” “time taken”) may be numerically summed before the event attributes are included in the aggregated event.

FIG. 12A shows an exemplary screenshot in accordance with the disclosed embodiments. More specifically, FIG. 12A shows a screenshot of a GUI, such as GUI 1025 of FIG. 10. The GUI may be used with a risk-identification mechanism and/or a capture trigger, such as risk-identification mechanism 1007 and capture trigger 1009 of FIG. 10.

The GUI of FIG. 12A may include a portion 1202 that represents the risk-identification mechanism. For example, portion 1202 may display a dashboard of time-series event data that represents security risks. The dashboard includes a number of potential security risks, such as “HTTP Errors,” “DNS Errors,” “Cloud Email,” “NFS Activity,” and “Threat List Activity.” Events that match one of the listed potential security risks may be represented as bars within a time interval represented by the horizontal dimension of the dashboard. For example, a security risk 1206 may be shown as a series of bars clustered around an interval of time under “DNS Errors” in portion 1202.

On the other hand, the dashboard may lack data for other potential security risks because the data volume associated with capturing network data across all protocols and/or security risks may be too large to effectively store and/or consume. As a result, portion 1202 may indicate that no data is available (e.g., “Search returned no results”) for the “HTTP Errors,” “Cloud Email,” “NFS Activity,” and “Threat List Activity” security risks.

The GUI may also include a portion 1204 that represents a capture trigger for generating additional time-series event data based on identified security risks from portion 1202. For example, portion 1204 may include a checkbox that allows a user to activate the capture trigger upon identifying security risk 1206 in portion 1202. Portion 1204 may also include a first drop-down menu that allows the user to specify one or more protocols (e.g., “HTTP,” “DNS,” “All Email,” “NFS/SMB,” “All Protocols”) of additional time-series event data to be captured with the capture trigger. Portion 1204 may additionally include a second drop-down menu that allows the user to specify a period (e.g., “4 Hours”) over which the additional time-series event data is to be captured after the capture trigger is activated.

After the capture trigger is activated, configuration information on one or more remote capture agents used to generate the time-series event data may be updated to include the additional protocol(s) specified in portion 1204. For example, configuration information for configuring the generation of additional event streams from the specified protocol(s) may be propagated to the remote capture agents, and the remote capture agents may use the configuration to create the event streams from network data and/or event data at the remote capture agents. The configuration information may include default event attributes for the protocol(s) and/or event attributes that may be of interest to the security assessment of network packet flows. For example, the configuration information may specify the generation of event data related to other security risks, such as the security risks shown in the dashboard. Once the event data is generated and/or indexed, the event data may be shown in the dashboard to facilitate verification, monitoring, and/or analysis of the security risk. After the pre-specified period obtained from portion 1204 has passed, the configuration information on the remote capture agents may be updated to disable the generation of the additional event streams and reduce the volume of network data captured by the remote capture agents.

As with the user interfaces of FIGS. 11A-11E, the user may add one or more filters that are applied during the generation of the additional time-series event data. For example, the user may use the user interfaces of FIGS. 11D-11E to add a filter for network and/or event data that exactly matches the IP address (e.g., 10.160.26.206) from which the security risk was detected. As a result, the additional time-series data may be generated only from network data containing the same source IP address. The user may also use the user interfaces of FIGS. 11A-11C to customize the collection of additional time-series event data by protocol and/or event attributes.

FIG. 12B shows an exemplary screenshot in accordance with the disclosed embodiments. In particular, FIG. 12B shows a screenshot of a GUI, such as GUI 1025 of FIG. 10. Like the GUI of FIG. 12A, the GUI of FIG. 12B includes a first portion 1206 representing a risk-identification mechanism and a second portion 1208 representing a capture trigger.

Portion 1206 may allow a user to create a recurring search for time-series event data that matches a security risk. For example, portion 1206 may include user-interface elements for obtaining a domain, application context, description, search terms, time range (e.g., start and end times), and/or frequency (e.g., daily, hourly, every five minutes, etc.) for the recurring search. The user may use the user-interface elements of portion 1206 to specify a recurring search for an excessive number of failed login attempts in captured network and/or event data, which may represent brute force access behavior that constitutes a security risk.

Portion 1208 may allow the user to provide the capture trigger, which is automatically activated if the recurring search finds time-series event data that matches the security risk. As with portion 1204 of FIG. 12A, portion 1208 may allow the user to set the capture trigger, specify one or more protocols to be captured with the capture trigger, and/or a pre-specified period over which network data using the protocol(s) is to be captured.

After the user has finished defining the recurring search and capture trigger, the user may select a user-interface 1210 (e.g., “Save”) to save the recurring search and capture trigger. The capture trigger may then be activated without additional input from the user once an iteration of the recurring search identifies the security risk. Conversely, the user may select a user-interface element 1212 (e.g., “Cancel”) to exit the screen of FIG. 12B without creating the recurring search and/or capture trigger.

FIG. 13 shows a flowchart illustrating the processing of network data in accordance with the disclosed embodiments. In one or more embodiments, one or more of the steps may be omitted, repeated, and/or performed in a different order. Accordingly, the specific arrangement of steps shown in FIG. 13 should not be construed as limiting the scope of the embodiments.

Initially, configuration information is obtained at a remote capture agent from a configuration server over a network (operation 1302). The remote capture agent may be located on a separate network from that of the configuration server. For example, the remote capture agent may be installed on a physical and/or virtual machine on a remote network and/or cloud. As discussed above, the remote capture agent and other remote capture agents may be used to capture network data from a set of remote networks in a distributed manner.

Next, the configuration information is used to configure the generation of event data from network packets captured by the remote capture agent during the runtime of the remote capture agent (operation 1304). For example, the configuration information may be used to configure the remote capture agent to identify certain types of network packets, extract network data from the network packets, and/or include the network data in the event data.

The remote capture agent may identify network packets in a packet flow based on control information in the network packets (operation 1306). For example, network packets between a source and destination may be identified based on source and/or destination network addresses, source and/or destination ports, and/or transport layer protocols in the headers of the network packets.

The remote capture agent may also assemble the packet flow from the network packets (operation 1308) and/or decrypt the network packets upon detecting encryption of the network packets (operation 1310). For example, the remote capture agent may rearrange out-of-order TCP packets into a TCP stream. The remote capture agent may also analyze the byte signatures of the network packets' payloads to identify encryption of the network packets and use an available private key to decrypt the network packets.

After the packet flow is identified, assembled and/or decrypted, the remote capture agent may obtain a protocol classification for the packet flow (operation 1312). For example, the remote capture agent may provide network packets in the packet flow to a protocol-decoding mechanism and receive one or more protocol identifiers representing the protocols used by the network packets from the protocol-decoding mechanism.

Next, the remote capture agent may use configuration information associated with the protocol classification to build an event stream from the packet flow (operation 1314), as described in further detail below with respect to FIG. 14. The remote capture agent may then transmit the event stream over a network for subsequent storage and processing of the event stream by one or more components on the network (operation 1316). For example, the remote capture agent may transmit the event stream to one or more data storage servers, configuration servers, and/or indexers on the network.

An update to the configuration information may be received (operation 1316). For example, the remote capture agent may receive an update to the configuration information after the configuration information is modified at a configuration server. If an update to the configuration information is received, the update is used to reconfigure the generation of time-series event data at the remote capture agent during runtime of the remote capture agent (operation 1320). For example, the remote capture agent may be use the updated configuration information to generate one or more new event streams, discontinue the generation of one or more existing event streams, and/or modify the generation of one or more existing event streams.

The remote capture agent may continue to be used (operation 1322) to capture network data. If the remote capture agent is to be used, packet flows captured by the remote capture agent are identified (operation 1306), and network packets in the packet flows are assembled into the packet flows and/or decrypted (operations 1308-1310). Protocol classifications for the packet flows are also obtained and used, along with configuration information associated with the protocol classifications, to build event streams from the packet flows (operations 1312-1314). The event streams are then transmitted over the network (operation 1316), and any updates to the configuration information are used to reconfigure the operation of the remote capture agent (operations 1318-1320) during generation of the event streams. Capture of network data by the remote capture agent may continue until the remote capture agent is no longer used to generate event data from network data.

FIG. 14 shows a flowchart illustrating the process of using configuration information associated with a protocol classification to build an event stream from a packet flow in accordance with the disclosed embodiments. In one or more embodiments, one or more of the steps may be omitted, repeated, and/or performed in a different order. Accordingly, the specific arrangement of steps shown in FIG. 14 should not be construed as limiting the scope of the embodiments.

First, one or more event attributes associated with the protocol classification are obtained from the configuration information (operation 1402). For example, the event attribute(s) may be obtained from a portion of the configuration information that specifies the generation of an event stream from network data matching the protocol classification.

Next, the event attribute(s) are extracted from network packets in the packet flow (operation 1404). For example, the event attribute(s) may be used to generate event data from the network packets. The configuration information may optionally be used to transform the extracted event attribute(s) (operation 1406). For example, the configuration information may be used to aggregate the event data into aggregated event data that reduces the volume of event data generated while retaining the important aspects of the event data.

Finally, the extracted and/or transformed event attributes are included in the event stream (operation 1408). For example, the event stream may be include a series of events and/or aggregated events that contain event attributes that are relevant to the protocol classification of the network packets represented by the events.

FIG. 15 shows a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments. In one or more embodiments, one or more of the steps may be omitted, repeated, and/or performed in a different order. Accordingly, the specific arrangement of steps shown in FIG. 15 should not be construed as limiting the scope of the embodiments.

First, a GUI for obtaining configuration information for configuring the generation of time-series event data from network packets captured by one or more remote agents is provided (operation 1502). The GUI may include a number of user-interface elements for streamlining the creation and/or update of the configuration information. First, the GUI may provide a set of user-interface elements for including one or more event attributes in the time-series event data of an event stream associated with a protocol classification of the network packets (operation 1504). For example, the GUI may include a set of checkboxes that enable the selection of individual event attributes for inclusion in the time-series event data.

Second, the GUI may provide a set of user-interface elements for managing the event stream (operation 1506) and/or obtaining the protocol classification for the event stream. For example, the GUI may include one or more user-interface elements for cloning the event stream from an existing event stream, which imparts the protocol classification of the existing event stream on the cloned event stream. The GUI may also include user-interface elements for deleting the event stream, enabling the event stream, and/or disabling the event stream.

Third, the GUI may provide a set of user-interface elements for filtering the network packets (operation 1508) prior to generating the time-series event data from the network packets. Each filter may identify an event attribute, a comparison to be performed on the event attribute, and/or a value to which the event attribute is to be compared. For example, the filter may match the event attribute to a Boolean value (e.g., true or false), perform a numeric comparison (e.g., equals, greater, less than, greater than or equal to, less than or equal to), and/or verify the definition of (e.g., the existence of) the event attribute in network data. The filter may also compare the event attribute to a regular expression, perform an exact match of the event attribute to the value, perform a partial match of the event attribute to the value, and/or determine the event attribute's position in an ordering.

Fourth, the GUI may provide a set of user-interface elements for aggregating the event attribute(s) into aggregated event data that is included in the event stream (operation 1510). For example, the GUI may provide user-interface elements for identifying event attributes as key attributes used to generate a key representing the aggregated event data and/or aggregation attributes to be aggregated prior to inclusion in the aggregated event data. The GUI may also include one or more user-interface elements for obtaining an aggregation interval over which the one or more event attributes are aggregated into the aggregated event data.

Finally, the event attribute(s), protocol classification, filtering information, and/or aggregation information obtained from the GUI are included in the configuration information (operation 1512). The configuration information may then be used to configure the protocol-based capture, filtering, and/or aggregation of network data at the remote capture agent(s).

FIG. 16 shows a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments. In one or more embodiments, one or more of the steps may be omitted, repeated, and/or performed in a different order. Accordingly, the specific arrangement of steps shown in FIG. 16 should not be construed as limiting the scope of the embodiments.

Initially, a risk-identification mechanism for identifying a security risk from time-series event data generated from network packets captured by one or more remote capture agents distributed across a network is provided (operation 1602). The risk-identification mechanism may include a GUI that displays an event of interest related to the security risk. For example, the GUI may show potential security risks in a dashboard and/or other visualization of the time-series event data. Alternatively, the risk-identification mechanism may include a search and/or recurring search for a subset of the time-series event data matching the security risk. For example, the risk-identification mechanism may include a search mechanism that allows a user to search for threats, attacks, errors, and/or other notable events in the time-series event data.

Next, a capture trigger for generation additional time-series data from the network packets on the remote capture agent(s) based on the security risk is provided (operation 1604). The capture trigger may be received through one or more user-interface elements of a GUI, such as the same GUI used to provide the risk-identification mechanism. For example, the capture trigger may be activated in a portion of the GUI that is above, below, and/or next to a dashboard that displays security risks to the user. Alternatively, the capture trigger may be linked to a recurring search for time-series event data that matches a security risk. As a result, the capture trigger may automatically be activated once time-series event data matching the security risk is found.

After the capture trigger is activated, the capture trigger is used to configure the generation of the additional time-series event data from the network packets (operation 1606). For example, activation of the capture trigger may result in the updating of configuration information for the remote capture agent(s), which causes the remote capture agent(s) to generate additional event streams containing event attributes associated with protocols that facilitate analysis of the security risk.

Finally, generation of the additional time-series event data is disabled after a pre-specified period has passed (operation 1608). For example, generation of the additional time-series event data may be set to expire a number of hours or days after the capture trigger is activated. The expiry may be set by the user and/or based on a default expiration for security-based capture of additional network data from network packets.

FIG. 17A shows an exemplary screenshot in accordance with the disclosed embodiments. More specifically, FIG. 17A shows a screenshot of a GUI, such as GUI 1025 of FIG. 10. As described above, the GUI may be used to obtain configuration information that is used to configure the generation of event streams containing time-series event data at one or more remote capture agents distributed across a network.

As shown in FIG. 17A, the GUI includes a table with a set of columns 1708-1724. Columns 1710-1718 may include high-level event stream information related to event streams that are created and/or managed using the configuration information. Each row of the table may represent an event stream, and rows of the table may be sorted by column 1710.

Column 1710 shows an alphabetized list of names of the event streams, and column 1714 may specify a protocol associated with each event stream. For example, columns 1710 and 1714 may include names and/or protocols of event streams generated from HTTP, Dynamic Host Configuration Protocol (DHCP), DNS, FTP, email protocols, database protocols, NFS, Secure Message Block (SMB), security protocols, Session Initiation Protocol (SIP), TCP, and/or UDP network traffic. In other words, the event streams may be generated based on transport layer protocols, session layer protocols, presentation layer protocols, and/or application layer protocols.

A user may select a name of an event stream under column 1710 to access and/or update configuration information for configuring the generation of the event stream. For example, the user may select “Stream_A” in column 1710 to navigate to a screen of the GUI that allows the user to specify event attributes, filters, and/or aggregation information related to creating the “Stream_A” event stream.

Column 1712 specifies a type indicating whether each event stream is cloned from an existing event stream. For example, column 1712 may indicate that the “Stream_C” and “Stream_D” streams have been cloned (e.g., copied) from other event streams, while the remaining event streams may be predefined with default event attributes.

Column 1716 shows an application associated with each event stream, and column 1718 includes a description of each event stream. For example, column 1716 may include the names of applications used to create the event streams (e.g., “Stream,” “Enterprise Security,” etc.), and column 1718 may include descriptions that are generated by the applications and/or users of the applications.

The table may also include a column 1722 that specifies a status indicating whether each event stream is enabled or disabled. For example, column 1722 may indicate that the “Stream_A,” “Stream_B,” “Stream_C,” and “Stream_E” event streams are enabled. If an event stream is enabled, time-series event data may be included in the event stream based on the configuration information for the event stream. If an event stream is disabled, time-series event data may not be generated for the event stream.

Event streams in the table may further be sorted by information in other columns of the table and/or randomly. For example, the user may select the column header of a given column (e.g., columns 1708-1724) to order the displayed event stream information by the information represented by the column. Alternatively, event streams in the table may be randomly sorted and/or sorted by an attribute that is not explicitly displayed in a column of the table.

The GUI also includes a user-interface element 1730 (e.g., “Clone Stream”). A user may select user-interface element 1730 to create a new event stream as a copy of an event stream listed in the GUI. After user-interface element 1730 is selected, an overlay may be displayed that allows the user to specify a name for the new event stream, a description of the new event stream, and an existing event stream from which the new event stream is to be cloned. The new event stream may then be created with the same event attributes and/or configuration options as the existing event stream, and the GUI may navigate the user to a new screen for customizing the new event stream as a variant of the existing event stream (e.g., by adding or removing event attributes, filters, and/or aggregation information).

As mentioned above, the GUI may include functionality to group event information for the event streams by one or more event stream attributes. In particular, the GUI may provide a user-interface element 1704 for specifying an event stream attribute by which event streams are to be grouped. For example, user-interface element 1704 may be a drop-down menu that allows the user to specify grouping of the event streams by “Protocol,” “Category,” or “Apps” (e.g., applications). As shown in FIG. 17A, the “Protocol” event stream attribute is specified in user-interface element 1704. In response to the selection of “Protocol” in user-interface element 1704, the GUI may display a list 1702 of possible values for the “Protocol” event stream attribute. For example, list 1702 may include protocols such as HTTP, FTP, TCP, UDP, and SMTP.

The user may select a protocol name from list 1702 to view event stream information for a subset of the event streams matching the protocol (e.g., event streams containing time-series event data generated from network packets classified as using the protocol). Because “HTTP” is selected in list 1702, the table may show event stream information for event streams that match the “HTTP” protocol classification, as indicated in column 1714. The user may select another protocol name from list 1702 to view event streams associated with another protocol represented by the protocol name, or the user may select “All” in list 1702 to view all event streams, regardless of the event streams' protocol classifications.

The GUI may additionally group the event stream information by an event stream lifecycle of the event streams. In particular, the GUI may include two user-interface elements 1766-1768 for specifying an event stream lifecycle. The user may select user-interface element 1766 (e.g., “Permanent”) to view event stream information for permanent event streams and user-interface element 1768 (e.g., “Ephemeral”) to view event stream information for ephemeral event streams. Selection of one user-interface element 1766-1768 may result in the automatic deselection of the other user-interface element. In response to the selection of either user-interface element 1766 or user-interface element 1768, the GUI may further group event stream information shown in the table by the event stream lifecycle represented by the selected user-interface element. For example, the GUI may show only permanent event streams that match the “HTTP” protocol classification in the table of FIG. 17A because user-interface element 1766 and “HTTP” are selected.

To further facilitate analysis and/or management of the event streams, column 1720 of the table may include graphs of metrics associated with the event streams inline with event stream information for the event streams. The graphs may be generated using time-series event data for the event streams. For example, column 1720 may show, for each event stream represented by a row in the table, a sparkline of network traffic over time for the event stream. Alternatively, column 1720 may show graphs and/or sparklines of other metrics, such as a number of events and/or a number of notable events over time. As with other columns in the table, column 1720 may be updated based on user interaction with user-interface elements 1704 and 1766-1768 and list 1702. For example, the selection of one or more other groupings using user-interface elements 1704 and 1766-1768 and list 1702 may trigger the display of event stream information and graphs in the table for event streams matching the other grouping(s).

The user may click on a graph in column 1720 to navigate to a screen containing a larger version of the graph and one or more user-interface elements for changing a view of the graph. For example, after the user selects a graph in column 1720, the GUI may navigate the user to a dashboard with a more detailed version of the graph that includes a scale and/or labeled axes. The dashboard may also include scrollbars, sliders, buttons, and/or other user-interface elements that allow the user to change the scale along one or both axes, scroll across different portions of the data (e.g., different time ranges), and/or view data from multiple event streams in the same graph.

The GUI may also include a user-interface element 1732 that shows an aggregated value of the metric in the graphs of column 1720. For example, user-interface element 1732 may include a sparkline of aggregate network traffic, events, and/or notable events over time for the event streams represented by the rows of the table. The aggregate metric may be calculated as a sum, average, and/or other summary statistic.

User-interface element 1732 may also display a numeric value of the aggregate metric (e.g., “154 Mb/s”) over the time spanned by the sparkline. For example, the numeric value shown to the left of the sparkline in user-interface element 1732 may represent a value of aggregate network traffic at a time represented by a given point in the sparkline. The user may position a cursor at different points along the sparkline to view different values of the aggregate network traffic represented by the points. Similarly, the user may position the cursor at different points in the graphs of column 1720 to trigger the display of numeric values of network traffic at times represented by those points.

As with graphs in column 1720, the graph in user-interface element 1732 may be generated or updated based on the event stream information shown in the table. For example, the selection of “UDP” in list 1702 may cause the GUI to display event stream information for event streams matching the “UDP” protocol classification. In turn, graphs in column 1720 and user-interface element 1732 may be updated to reflect network traffic, events, notable events, network bandwidth, total bandwidth, protocol-based bandwidth, and/or other metrics associated with the “UDP” event streams.

The graphs in column 1720 and/or user-interface element 1732 may further be updated in real-time with time-series event data as the time-series event data is received from one more remote capture agents. For example, sparklines and/or other graphical representations in column 1720 and user-interface element 1732 may shift as the time window spanned by the sparklines advances and additional time-series event data is collected within the time window.

In addition to displaying event stream information and graphs for one or more groupings of event streams, the GUI of FIG. 17A may enable management of the event streams through a column 1724 that allows the user to perform one or more actions on individual event streams. Each row of the table may include a user-interface element in column 1724 that, when selected, activates a drop-down menu of possible actions to be applied to the corresponding event stream.

Within the GUI, the user-interface element in the first row of column 1724 may be selected. As a result, a drop-down menu may be displayed below the user-interface element with a set of options, including “Disable,” “Clone,” and “Delete.” The user may select “Disable” to disable generation of the event stream from network data. Alternatively, the “Disable” option may be replaced with an “Enable” option if the event stream (e.g., “Stream_D”) is already disabled to allow the user to enable generation of the event stream from network data.

The user may select “Clone” to create a new event stream as a copy of the event stream. If “Clone” is selected, the GUI may obtain a name and description for the cloned event stream. The GUI may then copy configuration information for the event stream to a new screen for configuring the cloned event stream. As described in the above-referenced application, configuration of new and/or cloned event streams may include one or more event attributes to be included in the event stream, filtering network packets prior to generating the event stream from the network packets, and/or aggregating the event attribute(s) into aggregated event data that is included in the event stream.

The user may select “Delete” to delete the event stream. If “Delete” is selected, the GUI may remove event stream information for the event stream from the table. In turn, a representation of the event stream may be removed from the configuration information to stop the generation of time-series event data in the event stream by one or more remote capture agents.

The GUI may additionally include a user-interface element 1726 (e.g., “Bulk Edit”) that allows the user to apply an action associated with managing an event stream to multiple event streams. The user may use a set of checkboxes in column 1708 to select one or more event streams to which the action is to be applied. The user may then select user-interface element 1726 to access a drop-down menu containing a set of possible actions to apply to the selected event streams. For example, the drop-down menu may include options for enabling, disabling, and deleting the selected event streams, which are similar to options in the drop-down menu of user-interface elements in column 1724.

Finally, the GUI may include a user-interface element 1728 that allows the user to search for event streams. The user may type one or more keywords into a text box provided by user-interface element 1728, and the GUI may match the keyword(s) to the names, descriptions, and/or other event stream attributes of the event streams in the table. Event stream information for event streams that do not match the keyword(s) may be removed from the table while the search is in effect. User-interface element 1728 may thus provide another mechanism by which event stream information in the table can be grouped and/or filtered. Consequently, the GUI of FIG. 17A may allow the user to create, find, and/or manage event streams across multiple applications, categories, keywords, and/or protocols that may be relevant to the user's interests or goals.

FIG. 17B shows an exemplary screenshot in accordance with the disclosed embodiments. More specifically, FIG. 17B shows the GUI of FIG. 17A after the event stream attribute in user-interface element 1704 is changed from “Protocol” to “Category.” In response to the change, the GUI may update list 1702 with possible values for the “Category” event stream attribute. For example, list 1702 may include different technological categories of network data represented by the event streams, such as “Infrastructure,” “Networking,” “File Transfer,” “Web,” and “Email.” As with the GUI of FIG. 17A, the user may select “All” in list 1702 to view all event streams, regardless of the categories to which the event streams belong.

Other categories not shown in list 1702 may include, but are not limited to, messaging, authentication, database, telephony, and/or network management. Finally, categories in list 1702 may include one or more user-created values. For example, the GUI may provide one or more user-interface elements that allow the user to specify a name of a new category, along with one or more event stream attributes of event streams to be included under the new category.

Within list 1702, “Networking” is selected. As a result, the table may include names, types, protocols, applications, descriptions, and/or other event stream information for permanent event streams in a networking category, such as event streams associated with networking protocols (e.g., DHCP, DNS, TCP, UDP). Sparklines in column 1720 and user-interface element 1732 may also be updated to reflect metrics and an aggregated metric associated with the event streams represented by the rows of the table in FIG. 17B, respectively.

FIG. 17C shows an exemplary screenshot in accordance with the disclosed embodiments. More specifically, FIG. 17C shows the GUI of FIG. 17B after user-interface element 1768 has been selected and the event stream attribute in user-interface element 1704 is changed from “Category” to “Apps.” In the GUI of FIG. 17C, list 1702 includes possible values for the “Apps” (e.g., applications) event stream attribute. For example, list 1702 may include names of applications (e.g., “Stream,” “Enterprise Security”) associated with event streams, such as applications from which the event streams were created. Within list 1702, “Enterprise Security” is selected. The user may select another application name from list 1702 to view event streams associated with another application represented by the application name, or the user may select “All” in list 1702 to view all event streams, regardless of the applications associated with the event streams.

Because user-interface element 1768 is also selected, the table includes event stream information for ephemeral event streams. In other words, the GUI may group event streams by application and event stream lifecycle so that event stream information for event streams that match both the “Enterprise Security” application name and the ephemeral event stream lifecycle is shown in the table.

The user may apply an additional grouping or filter to event stream information shown in the table by performing a search using user-interface element 1728. For example, the user may type one or more keywords into a text box provided by user-interface element 1728, and the GUI may match the keyword(s) to the names, descriptions, and/or other event stream attributes of the ephemeral event streams in the table. Event stream information for event streams that do not match the keyword(s) may be removed from the table while the search is in effect.

As shown in FIG. 17C, the table includes a different set of columns 1734-1750 from the table of FIGS. 17A-17B. Unlike columns 1710-1718 of FIGS. 17A-17B, columns 1734-1746 may include event stream information that is relevant to ephemeral event streams instead of permanent event streams. Column 1734 may show an alphabetized or otherwise ordered or unordered list of names of groups of ephemeral event streams, and column 1736 may show the number of event streams in each group. The user may select a user-interface element in a row of column 1750 to expand the table to show event stream information for ephemeral event streams in the group represented by the row. For example, the user may select the user-interface element in the first row of column 1750 to view event stream information for 120 ephemeral event streams belonging to the group named “Group_A,” as discussed in further detail below with respect to FIG. 17D.

The user may also select a value in column 1734 to view time-series event data for the corresponding ephemeral event stream or group of ephemeral event streams. For example, selection of the “Group_A” value in column 1734 may cause the GUI to navigate to a screen showing events and the corresponding timestamps of the ephemeral event streams of “Group_A,” graphs of metrics related to the events, and/or other information associated with the events.

Column 1738 may show the names of applications used to create the ephemeral event streams. Because “Enterprise Security” is selected in list 1702, all values in column 1738 are matched to the “Enterprise Security” application name, and event stream information for ephemeral event streams associated with other applications (e.g., “Stream”) is omitted from the table.

In addition, column 1738 may allow the user to navigate from the event stream information for a given ephemeral event stream to creation information for a creator of the ephemeral event stream. For example, each application name in column 1738 may include a hyperlink to a screen of the GUI for interacting with the application represented by the application name. The screen may show user-interface elements and/or information that provides context for the creation of the ephemeral event stream. As a result, column 1738 may link the portion of the GUI used to manage the ephemeral event stream to the portion of the GUI used to create the ephemeral event stream, which is described in further detail below with respect to FIG. 17D.

Columns 1740-1744 show start times, end times, and times remaining for the ephemeral event streams, respectively. The start times may represent times at which generation of time-series event data for the corresponding ephemeral event streams was initiated. For example, each start time may be a time at which an ephemeral event stream was created by a capture trigger for generating additional time-series event data based on a security risk and/or an application that collects time-series event data from a number of sources for subsequent analysis and/or correlation.

The end times may be times at which generation of time-series event data for the corresponding ephemeral event streams is to end. For example, each end time may be a time that is a pre-specified number of minutes, hours, and/or days from the corresponding start time. The amount of time spanned between the start and end time may thus represent the duration of the ephemeral event stream, which may be selected by a capture trigger, application, and/or user interacting with the capture trigger or application. Once the end time for an ephemeral event stream is reached, the ephemeral event stream is terminated.

The times remaining for the ephemeral event streams may indicate the amount of time left in the lifetimes of the ephemeral event streams. For example, each value in column 1744 may represent a “countdown” to the end time of the corresponding ephemeral event stream shown in column 1742.

Column 1746 may provide a status indicating whether each ephemeral event stream or group of ephemeral event streams is enabled or disabled. For example, column 1746 may indicate that the “Group_A,” “Group_B,” and “Group_E” groups of ephemeral event streams are enabled. Such enabling or disabling of ephemeral event streams may be independent of the creation or termination of the ephemeral event streams. For example, an ephemeral event stream may be created at the start time of the ephemeral event stream by updating one or more remote capture agents with configuration information for the ephemeral event stream. Between the start and end times of the ephemeral event stream, the ephemeral event stream may be disabled to stop the generation of time-series event data for the ephemeral event stream and/or re-enabled to resume the generation of time-series event data for the ephemeral event stream. Once the end time of the ephemeral event stream is reached, the ephemeral event stream may be terminated, and a representation of the event stream may be removed from the configuration information to stop the generation of time-series event data in the event stream by the remote capture agent(s).

Like the GUIs of FIGS. 17A-17B, the GUI may provide a number of mechanisms for managing the ephemeral event streams. First, a column 1748 in the table may allow the user to perform one or more actions on individual event streams. Each row of the table may include a user-interface element in column 1748 that, when selected, activates a drop-down menu of possible actions to be applied to the corresponding event stream.

Within the GUI, the user-interface element in the first row of column 1748 may be selected. As a result, a drop-down menu may be displayed below the user-interface element with a set of options, including “Disable,” “Delete,” and “Modify End Time.” The user may select “Disable” to disable generation of the ephemeral event stream from network data before the end time of the ephemeral event stream is reached. Alternatively, the “Disable” option may be replaced with an “Enable” option if the event stream (e.g., “Stream_D”) is already disabled to allow the user to enable generation of the event stream from network data before the end time of the ephemeral event stream is reached.

The user may select “Delete” to delete the ephemeral event stream. If “Delete” is selected, the GUI may remove the ephemeral event stream from the table, even if the end time of the ephemeral event stream has not been reached. In turn, a representation of the event stream may be removed from the configuration information to stop the generation of time-series event data in the event stream by one or more remote capture agents.

The user may select “Modify End Time” to modify the end time of the ephemeral event stream shown in column 1742. If “Modify End Time” is selected, the GUI may display an overlay that allows the user to specify a new end time for the ephemeral event stream as a date and time and/or a number of minutes, hours, and/or days by which the existing end time should be extended or reduced.

Second, user-interface element 1726 (e.g., “Bulk Edit”) may be used to apply an action associated with managing the event streams to multiple ephemeral event streams. The user may use a set of checkboxes in column 1708 to select the event streams to which the action is to be applied. The user may then select user-interface element 1726 to access a drop-down menu containing a set of possible actions to apply to the selected event streams. For example, the drop-down menu may include options for enabling, disabling, and deleting the selected event streams, which are similar to options in the drop-down menu of user-interface elements in column 1746.

Third, user-interface element 1730 may allow the user to create a new ephemeral event stream as a copy of an existing ephemeral event stream. After user-interface element 1730 is selected, an overlay may be displayed that includes user-interface elements for specifying a name for the new ephemeral event stream, a description of the new event stream, and an existing ephemeral event stream from which the new ephemeral event stream is to be cloned. The new ephemeral event stream may be created with the same event attributes and/or configuration options as the existing ephemeral event stream, including the same end time and/or duration as the existing ephemeral event stream. The GUI may then show a new screen that allows the user to customize the new ephemeral event stream as a variant of the existing ephemeral event stream.

FIG. 17D shows an exemplary screenshot in accordance with the disclosed embodiments. More specifically, FIG. 17D shows the GUI of FIG. 17C after the user-interface element in the first row of column 1750 has been selected. In response to the selected user-interface element, the table includes additional event stream information for ephemeral event streams in the group represented by the first row in the table. As shown in FIG. 17D, the additional event stream information includes an additional grouping of ephemeral event streams in the “Group_A” group by protocol. For example, the GUI may indicate that the 120 ephemeral event streams in the group are further grouped into 80 ephemeral event streams for capturing HTTP network packets, 20 ephemeral event streams for capturing FTP network packets, and 20 ephemeral event streams for capturing UDP packets.

All ephemeral event streams in the group may be created by the same application (e.g., “Enterprise Security”) and have the same start and end times. As a result, the ephemeral event streams may be created by the application for the same purpose or similar purposes. For example, the “Enterprise Security” application may create 120 ephemeral event streams for generating additional time-series event data from network packets based on a security risk.

FIG. 17E shows an exemplary screenshot in accordance with the disclosed embodiments. In particular, FIG. 17E shows the GUI of FIG. 17C after a hyperlink (e.g., “Enterprise Security”) in the second row of column 1738 has been selected. The hyperlink may navigate the user from a screen for managing the ephemeral event stream represented by the second row of the table to a screen containing creation information for a creator of the ephemeral event stream.

The GUI of FIG. 17E may show a creator name (e.g., “Enterprise Security: Asset Investigator”) of the creator. For example, the name may specify an application (e.g., “Enterprise Security”) and/or a feature of the application (e.g., “Asset Investigator”) used to create the ephemeral event stream. The GUI may also include a portion 1758 that shows a trigger condition for creating or activating the ephemeral event stream. For example, portion 1758 may be a risk-identification mechanism that displays a dashboard of time-series event data representing security risks. The dashboard includes a number of potential security risks, such as “HTTP Errors,” “DNS Errors,” “Cloud Email,” “NFS Activity,” and “Threat List Activity.” Events that match one of the listed potential security risks may be represented as bars within a time interval represented by the horizontal dimension of the dashboard. For example, a security risk 1752 may be shown as a series of bars clustered around an interval of time under “DNS Errors” in portion 1758. The presence of security risk 1752 in portion 1758 may indicate that the trigger condition for creating the ephemeral event stream includes a potential security risk 1752, as discovered using portion 1758 in the “Enterprise Security” application. To enable identification of the trigger condition, portion 1758 may replicate the timescale and data (e.g., security risk 1752) seen by the user at the time at which the ephemeral event stream was created using the “Enterprise Security” application.

Below portion 1758, the GUI may display additional creation information 1754 describing the creator of the ephemeral event stream. For example, creation information 1754 may include a start time (e.g., “2014/09/01 12:00:00”), duration (e.g., “7 days”), and/or protocol (e.g., “HTTP”) associated with network data capture by the ephemeral event stream. Creation information 1754 may describe a capture trigger for generating additional time-series event data based on identified security risks from portion 1758. For example, creation information 1754 may be submitted through one or more user-interface elements shown below portion 1758 in the “Enterprise Security” application to trigger the capture of additional time-series event data in response to security risk 1752. After creation information 1754 is submitted to the GUI, the information may be used to configure the generation of the ephemeral event stream at one or more remote capture agents.

The GUI may also include a hyperlink 1756 (e.g., “Go to Stream Configuration”) that navigates the user back to event stream information for the ephemeral event stream. For example, the user may select hyperlink 1756 to view the event stream information within the GUI of FIG. 17C. Hyperlinks in the GUIs of FIGS. 17C-17D may thus provide a mechanism for bidirectional navigation between the event stream information and the creation information. Such bidirectional linking may allow the user to establish the context for creating the ephemeral event stream as well as the current state of the ephemeral event stream, thus improving analysis, understanding, and management of ephemeral event streams from multiple disparate creators.

FIG. 18 shows a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments. More specifically, FIG. 18 shows a flowchart of grouping and managing event streams generated from captured network data. In one or more embodiments, one or more of the steps may be omitted, repeated, and/or performed in a different order. Accordingly, the specific arrangement of steps shown in FIG. 18 should not be construed as limiting the scope of the embodiments.

Initially, a GUI is provided on a computer system for configuring the generation of time-series event data from network packets captured by one or more remote capture agents (operation 1802). The GUI may include a number of user-interface elements for streamlining the creation, management, and/or update of the configuration information.

First, the GUI may provide a set of user-interface elements for specifying a grouping of a set of event streams containing time-series event data by an event stream attribute (operation 1804). For example, the GUI may enable grouping of the event streams by a category (e.g., web, infrastructure, networking, file transfer, email, messaging, authentication, database, telephony, network management, user-created value, etc.) and/or a protocol used by the network packets (e.g., transport layer protocol, session layer protocol, presentation layer protocol, application layer protocol). The GUI may also enable grouping of the event streams by applications used to create the event streams (e.g., based on application name) and/or event stream lifecycles of the event stream (e.g., permanent or ephemeral).

Next, the GUI may display a set of user-interface elements containing event stream information for one or more subsets of the event streams represented by the grouping of the event streams by the event stream attribute (operation 1806). Grouping of displayed event stream information by event stream attributes is described in further detail below with respect to FIG. 19.

Finally, the GUI may provide a set of user-interface elements for managing the event streams (operation 1808). For example, the GUI may be used to clone a new event stream from an existing event stream, create an event stream, delete an event stream, enable an event stream, disable an event stream, and/or modify an end time of an ephemeral event stream, as discussed above with respect to FIGS. 17A-17E.

FIG. 19 shows a flowchart illustrating the process of displaying event stream information represented by a grouping of the event streams by an event stream attribute in accordance with the disclosed embodiments. In one or more embodiments, one or more of the steps may be omitted, repeated, and/or performed in a different order. Accordingly, the specific arrangement of steps shown in FIG. 19 should not be construed as limiting the scope of the embodiments.

First, one or more values of an event stream attribute are displayed (operation 1902), and displayed event stream information is grouped into one or more subsets of the event streams based on the value(s) of the event stream attribute (operation 1904). For example, a user may specify the type of event stream attribute to group by, and a GUI may display one or more categories, protocols, application names, and/or other values of the event stream attribute in a list. After a given value of the event stream attribute is selected from the list, the GUI may show event stream information matching the selected value in a table next to the list.

The event stream information may be grouped by an additional event stream attribute (operation 1906). If the event stream information is not to be grouped by an additional event stream attribute, grouping of the displayed event stream information by the first event stream attribute is maintained.

If the event stream information is to be grouped by an additional event stream attribute, one or more values of the additional event stream attribute are displayed (operation 1902), and the displayed event stream information is further grouped into one or more additional subsets of the event streams based on the value(s) of the additional event stream attribute (operation 1904). Continuing with the above example, the event stream information in the table may additionally be grouped and/or filtered by an event stream lifecycle of the event streams, which may be permanent or ephemeral. If a permanent event stream lifecycle is selected (e.g., through the GUI), event stream information for permanent event streams that match the value of the first event stream attribute (e.g., category, protocol, application) is shown. Such event stream information may include a name, a type, a protocol, an application, a description, a status, and/or a graph of a metric associated with the time-series event data of the event streams. If an ephemeral event stream lifecycle is selected, event stream information for ephemeral event streams that match the value of the first event stream attribute is shown. Such event stream information may include a name, a number of event streams, an application, a start time, an end time, a time remaining, and/or a status.

The displayed event stream information may continue to be grouped by additional event stream attributes (operation 1906) to further facilitate the creation, search, and/or management of event streams across multiple applications, categories, protocols, and/or other event stream attributes. For example, the displayed event stream information may be grouped by category, protocol, keyword, and/or event stream lifecycle to allow a user to find event streams associated with a given category, protocol, keyword, and/or event stream lifecycle. For each event stream attribute by which the event stream information is to be grouped, one or more values of the event stream attribute are displayed (operation 1902). The displayed event stream information, which may already be grouped by one or more other event stream attributes, is then further grouped or filtered into one or more subsets of the event streams based on the value(s) of the event stream attribute (operation 1904). Grouping of displayed event stream information by values of event stream attributes may continue until the displayed event stream information has been grouped by values for all relevant event stream attributes.

FIG. 20 shows a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments. More specifically, FIG. 20 shows a flowchart of providing inline visualizations of metrics related to captured network data. In one or more embodiments, one or more of the steps may be omitted, repeated, and/or performed in a different order. Accordingly, the specific arrangement of steps shown in FIG. 20 should not be construed as limiting the scope of the embodiments.

Initially, a set of event streams is obtained from one or more remote capture agents over one or more networks (operation 2002). The event streams may include time-series event data generated from network packets captured by the remote capture agent(s). Next, event stream information for each event stream and a graph of a metric associated with the time-series event data in the event stream are displayed within a GUI on a computer system (operation 2004). The graph may include a sparkline, bar graph, line chart, histogram, and/or other type of visualization of the metric that is shown in line with the event stream information. The metric may include network traffic, a number of events, and/or a number of notable events (e.g., security risks).

A subset of event streams associated with a grouping of the event streams by an event stream attribute is also obtained (operation 2006). For example, the subset of event streams may match a value of one or more event stream attributes. Alternatively, the subset of event streams may include all event streams if the event streams are matched to all possible values of the event stream attribute(s). Next, the metric is aggregated across the subset of the event streams (operation 2008), and a graph of the aggregated metric across the event streams is displayed within the GUI (operation 2010). For example, the metric may be aggregated as a sum, average, and/or other summary statistic, and the graph of the aggregated metric may include a sparkline and/or other visual representation of the aggregated metric over time and/or another dimension.

While the graphs for individual event streams and the aggregated metric across the event streams are displayed, the graphs are updated in real-time with time-series event data from the remote capture agent(s) (operation 2012). For example, sparklines representing individual and aggregate network traffic over time may “advance” to reflect newly received time-series event data from the remote capture agent(s). The graph(s) are also updated with the value of the metrics or aggregated metric based on the position of a cursor over the graph(s) (operation 2014). For example, the numeric value of a metric (e.g., network traffic, number of events, number of notable events, etc.) at a given point in time may be displayed in response to the positioning of a cursor over that point in time in the graph.

Finally, event stream information for the subset of the event streams is displayed (operation 2016). For example, the event stream information may be displayed in a table, and the graphs for individual event streams may be shown in a column of the table. The graph of the aggregated metric may be displayed in a different part of the GUI, and the graphs may be updated based on the event streams and/or groupings shown in the table.

FIG. 21 shows a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments. More specifically, FIG. 21 shows a flowchart of managing ephemeral event streams generated from captured network data. In one or more embodiments, one or more of the steps may be omitted, repeated, and/or performed in a different order. Accordingly, the specific arrangement of steps shown in FIG. 21 should not be construed as limiting the scope of the embodiments.

First, a GUI is provided on a computer system for obtaining configuration information for configuring the generation of time-series event data from network packets captured by one or more remote capture agents (operation 2102). Next, a subset of one or more ephemeral event streams associated with a grouping of the ephemeral event stream(s) by an event stream attribute is obtained (operation 2104). For example, the subset of ephemeral event stream(s) may be associated with a grouping of the ephemeral event streams by one or more categories, applications and/or protocols. The ephemeral event streams may be used to temporarily generate time-series event data from network packets captured by the remote capture agent(s).

The GUI is used to display event stream information for the ephemeral event stream(s) (operation 2106), along with a set of user-interface elements for managing the ephemeral event stream(s) (operation 2108). The user-interface elements may be used to disable an ephemeral event stream, delete an ephemeral event stream, and/or modify an end time for terminating the ephemeral event stream. The event stream information may include a name, number of event streams, application, start time, end time, time remaining, and/or status.

The GUI also includes a set of user-interface elements for creating an ephemeral event stream (operation 2110), as well as a mechanism for applying an action associated with managing the ephemeral event stream(s) to a set of selected ephemeral event streams (operation 2112). For example, the GUI may enable the creation of an ephemeral event stream as a copy (e.g., clone) of an existing ephemeral event stream. The GUI may also allow multiple ephemeral event streams to be enabled, disabled, and/or deleted.

The configuration information is updated based on input received through the GUI (operation 2114) and provided over the network to the remote capture agent(s) (operation 2116). The configuration information may then be used to configure the generation of the time-series event data at the remote capture agent(s) during runtime of the remote capture agent(s). For example, the configuration information may be used to create, delete, enable, disable, and/or modify the end times of one or more ephemeral event streams.

FIG. 22 shows a flowchart illustrating the process of facilitating the processing of network data in accordance with the disclosed embodiments. More specifically, FIG. 22 shows a flowchart of bidirectional linking of ephemeral event streams to creators of the ephemeral event streams. In one or more embodiments, one or more of the steps may be omitted, repeated, and/or performed in a different order. Accordingly, the specific arrangement of steps shown in FIG. 22 should not be construed as limiting the scope of the embodiments.

First, a GUI is provided on a computer system for obtaining configuration information for configuring the generation of time-series event data from network packets captured by one or more remote capture agents (operation 2202). Next, a subset of one or more ephemeral event streams associated with a grouping of the ephemeral event stream(s) by an event stream attribute is obtained (operation 2204), and event stream information for the ephemeral event stream(s) is displayed in a GUI (operation 2206), as described above.

The GUI is used to provide a mechanism for navigating between the event stream information and creation information for one or more creators of the ephemeral event stream(s) (operation 2208). The mechanism may include a hyperlink from the event stream information to the creation information for a creator of an ephemeral event stream and/or a hyperlink from the creation information back to the event stream information. The creation information may include a creator name, a protocol, a duration of the ephemeral event stream, and/or a trigger condition for activating the ephemeral event stream. Creators of ephemeral event streams may include applications for monitoring network traffic captured by the remote capture agent(s) and/or capture triggers for generating additional time-series event data from network packets on the remote capture agent(s) based on a security risk.

The GUI also includes a set of user-interface elements containing the time-series event data (operation 2210). For example, the GUI may show individual events and the associated timestamps, graphs of metrics associated with the events, and/or other representations of events in the ephemeral event stream(s). Consequently, the GUI may facilitate understanding and analysis of both the content and context of the ephemeral event streams.

Custom Content Extraction

FIG. 23A presents a block diagram illustrating a system that facilitates custom content extraction from network packets in accordance with the disclosed embodiments. During operation, the system receives a packet stream 2302 to be processed. For example, this packet stream can be received at a remote capture agent situated at a network interface at a specific location within a network. Next, packet stream 2302 feeds into a multi-protocol packet parser 2304 that determines a protocol for the packet based on the structure of the packet. For example, in some embodiments, the multi-protocol packet parser 2304 comprises a deep-packet inspection (DPI) engine.

After the protocol for each packet is determined, the system feeds packets associated with one or more target protocols into a custom field parser 2308. Custom field parser 2308 applies one or more custom-content-extraction rules to each packet associated with a target protocol to obtain the extracted content 2310. In some embodiments, applying the custom-content-extraction rule to a given packet involves applying a field-specific regular expression to one or more target fields in the packet. In other embodiments, applying the custom-content-extraction rule to a given packet involves applying a field-specific extraction rule for a structured data format, which can include XML or JSON, to one or more target fields in the packet. Finally, the extracted content 2310 is formed into a set of events that are stored in data store 103 to facilitate queries involving the extracted content 2310. More specifically, as illustrated in FIG. 23B, the system obtains extracted content 2324 from a field 2322 in a packet 2320. Next, the system incorporates this extracted content 2324 into an event 2326, wherein event 2326 is subsequently stored in data store 103. Note that extracted content 2324 can comprise a portion of an event, an entire event, or can be split across multiple events.

At a later time, after extracted content 2310 is stored in data store 103, a user 2309 submits a query 2311 to a query processor 2314. Next, query processor 2314 applies the query to the extracted content 2310 retrieved from data store 103 to produce query results 2316. In some embodiments, the system uses a late-binding schema generated from query 2311 to retrieve data values from the events in the data store.

Processing Packets at Data-Ingestion Time

FIG. 24 presents a flowchart illustrating how the packets are processed to facilitate custom content extraction in accordance with the disclosed embodiments. During this process, the system receives a stream of packets (step 2402). The system then parses packets in the received stream to determine a protocol for each packet (step 2404). Next, the system applies a custom-content-extraction rule to each packet associated with a target protocol specified by the custom-content-extraction rule to obtain the extracted content (step 2406). Finally, the system stores the extracted content in events in a data store to facilitate subsequent queries involving the extracted content (step 2408).

Processing a Query

FIG. 25 presents a flowchart illustrating how a query is processed in accordance with the disclosed embodiments. First, the system receives a query to be applied to the events in the data store (step 2502). In response to this query, the system retrieves events from the data store (step 2504), and uses a late-binding schema generated from the query to retrieve data values from the events (step 2506). Finally, the system processes the query using the retrieved data values (step 2508).

Extraction Rule Dialog

FIG. 26 illustrates a dialog box 2602 that is configured to receive a custom-content-extraction rule from a user in accordance with the disclosed embodiments. Dialog box 2602 includes a number of fields for receiving input from a user, including: (1) a source field 2604 configured to receive an identifier for a source field for the extracted content in each packet; (2) an extraction type field 2606 configured to receive a specifier for a type of extraction rule to be used to obtain the extracted content (e.g., a regular expression or a rule specified in structured data format, such as XML or JSON); (3) an extraction rule field 2608 configured to receive an extraction rule entered by the user; (4) a new field 2610 configured to receive a new identifier that is used to identify the extracted content; and (5) a description field 2612 configured to receive a textual description of how the data is extracted.

Computer System

FIG. 27 shows a computer system 2700. Computer system 2700 includes a processor 2702, memory 2704, storage 2706, and/or other components found in electronic computing devices. Processor 2702 may support parallel processing and/or multi-threaded operation with other processors in computer system 2700. Computer system 2700 may also include input/output (I/O) devices such as a keyboard 2708, a mouse 2710, and a display 2712.

Computer system 2700 may include functionality to execute various components of the present embodiments. In particular, computer system 2700 may include an operating system (not shown) that coordinates the use of hardware and software resources on computer system 2700, as well as one or more applications that perform specialized tasks for the user. To perform tasks for the user, applications may obtain the use of hardware resources on computer system 2700 from the operating system, as well as interact with the user through a hardware and/or software framework provided by the operating system.

In one or more embodiments, computer system 2700 provides a system for facilitating the processing of network data. The system may include a configuration server. The configuration server may provide a GUI for obtaining configuration information for configuring the generation of time-series event data from network packets captured by a remote capture agent. The GUI may include a set of user-interface elements for specifying a grouping of a set of event streams containing the time-series event data by an event stream attribute associated with the event streams. The GUI may also include a set of user-interface elements containing event stream information for one or more subsets of the event streams represented by the grouping of the event streams by the event attribute. The GUI may further include graphs of metrics associated with the time-series event data in the event streams and a graph of an aggregated metric across the set of event streams.

The GUI may additionally include a set of user-interface elements for creating and managing the event streams, including one or more ephemeral event streams for temporarily generating time-series event data from network packets. Finally, the GUI may include a mechanism for navigating between event stream information for the ephemeral event streams and source information for one or more sources of the ephemeral event streams. Input received through the GUI may be used to update configuration information, which is provided over a network to the remote capture agent and used to configure the generation of time-series event data at the remote capture agent during runtime of the remote capture agent.

In addition, one or more components of computer system 2700 may be remotely located and connected to the other components over a network. Portions of the present embodiments (e.g., remote capture agent, configuration server, GUI, etc.) may also be located on different nodes of a distributed system that implements the embodiments. For example, the present embodiments may be implemented using a cloud computing system that manages the creation, update, and deletion of event streams at a set of distributed remote capture agents.

The foregoing descriptions of various embodiments have been presented only for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the forms disclosed. Accordingly, many modifications and variations will be apparent to practitioners skilled in the art. Additionally, the above disclosure is not intended to limit the present invention. 

What is claimed is:
 1. A computer-implemented method, comprising: receiving, via a graphical user interface (GUI), input defining a custom content extraction rule, wherein the input specifies: a source field in network packets to be monitored by a remote capture agent, wherein the source field contains structured data, an extraction rule to be used to extract data from the structured data to obtain extracted data, and a field name to be used to identify the extracted data in timestamped events to be generated by the remote capture agent; generating configuration information based on the input; sending the configuration information to the remote capture agent, wherein the configuration information causes the remote capture agent to generate timestamped events, wherein the timestamped events include extracted data obtained by applying the custom content extraction rule to network packets monitored by the remote capture agent, and wherein the extracted data is identified in the timestamped events using the field name; receiving the timestamped events from the remote capture agent, wherein each of the timestamped events includes extracted data identified by the field name; and storing the timestamped events in a data store, wherein storage of the timestamped events in the data store enables execution of queries based on the field name.
 2. The computer-implemented method of claim 1, wherein the remote capture agent generates a timestamped event of the timestamped events at least in part by: parsing a network packet of the network packets to identify a structure of the network packet, wherein the structure of the network packet is used to determine a protocol associated with the network packets; applying the extraction rule to the network packet to obtain extracted content, wherein applying the extraction rule includes: identifying the source field in the network packet containing the structured data from which the extracted content is to be obtained, and extracting data from the structured data contained in the source field of the network packet; generating a timestamped event including a field storing the extracted content; and sending the timestamped event including the extracted content to another component on a computer network for storage in a data store, the data store facilitating querying of timestamped event data stored in the data store using late-binding schemas generated from received queries.
 3. The computer-implemented method of claim 1, wherein the method further comprises: storing the timestamped events in a data store; receiving a query to be applied to the timestamped events stored in the data store; retrieving timestamped events from the data store satisfying the query; using a late-binding schema generated from the query to retrieve data values from the retrieved timestamped events; and processing the query using the retrieved data values.
 4. The computer-implemented method of claim 1, wherein the input further specifies a protocol to be associated with the custom content extraction rule.
 5. The computer-implemented method of claim 1, wherein the input further specifies a field-specific regular expression to be applied to the source field in the network packets, and wherein applying the custom content extraction rule to the network packets includes applying the field-specific regular expression to the source field in the network packets.
 6. The computer-implemented method of claim 1, wherein the structured data includes eXtensible Markup Language (XML) formatted data, and wherein applying the custom content extraction rule includes extracting data from the XML-formatted data.
 7. The computer-implemented method of claim 1, wherein the structured data includes JavaScript Object Notation (JSON) formatted data, and wherein applying the custom content extraction rule includes extracting data from the JSON-formatted data.
 8. The computer-implemented method of claim 1, wherein the custom content extraction rule is associated with a protocol, and wherein the remote capture agent uses a deep-packet inspection engine to determine that the network packets are associated with the protocol.
 9. The computer-implemented method of claim 1, wherein the input further specifies an extraction rule type that identifies a type of extraction rule to be used to obtain the extracted data.
 10. The computer-implemented method of claim 1, wherein the configuration information causes the remote capture agent to send the timestamped events to another component for storage in a data store.
 11. A non-transitory computer-readable storage medium storing instructions which, when executed by one or more processors, cause performance of operations comprising: receiving, via a graphical user interface (GUI), input defining a custom content extraction rule, wherein the input specifies: a source field in network packets to be monitored by a remote capture agent, the source field containing structured data, second input specifying an extraction rule to be used to extract data from the structured data to obtain extracted data, and a field name to be used to identify the extracted data in timestamped events to be generated by the remote capture agent; generating configuration information based on the input; sending the configuration information to the remote capture agent, wherein the configuration information causes the remote capture agent to generate timestamped events, wherein the timestamped events include extracted data obtained by applying the custom content extraction rule to network packets monitored by the remote capture agent, and wherein the extracted data is identified in the timestamped events using the field name receiving the timestamped events from the remote capture agent, wherein each of the timestamped events includes extracted data identified by the field name; and storing the timestamped events in a data store, wherein storage of the timestamped events in the data store enables execution of queries based on the field name.
 12. The non-transitory computer-readable storage medium of claim 11, wherein the remote capture agent generates the timestamped events at least in part by: parsing a network packet of the network packets to identify a structure of the network packet, wherein the structure of the network packet is used to determine a protocol associated with the network packet; applying the extraction rule to the network packet to obtain extracted content, wherein applying the extraction rule includes: identifying the source field in the network packet containing the structured data from which the extracted content is to be obtained, and extracting data from the structured data contained in the source field of the network packet; generating a timestamped event including a field storing the extracted content; and sending the timestamped event including the extracted content to another component on a computer network for storage in a data store, the data store facilitating querying of timestamped event data stored in the data store using late-binding schemas generated from received queries.
 13. The non-transitory computer-readable storage medium of claim 11, wherein the instructions, when executed by the one or more processors, further cause performance of operations comprising: storing the timestamped events in a data store; receiving a query to be applied to the timestamped events stored in the data store; retrieving timestamped events from the data store satisfying the query; using a late-binding schema generated from the query to retrieve data values from the retrieved timestamped events; and processing the query using the retrieved data values.
 14. The non-transitory computer-readable storage medium of claim 11, wherein the input further specifies a protocol to be associated with the custom content extraction rule.
 15. The non-transitory computer-readable storage medium of claim 11, wherein the input further specifies a field-specific regular expression to be applied to the source field in the network packets, and wherein applying the custom content extraction rule to the network packets includes applying the field-specific regular expression to the source field in the network packets.
 16. The non-transitory computer-readable storage medium of claim 11, wherein the structured data includes eXtensible Markup Language (XML) formatted data, and wherein applying the custom content extraction rule includes extracting data from the XML-formatted data.
 17. The non-transitory computer-readable storage medium of claim 11, wherein the structured data includes JavaScript Object Notation (JSON) formatted data, and wherein applying the custom content extraction rule includes extracting data from the JSON-formatted data.
 18. The non-transitory computer-readable storage medium of claim 11, wherein the custom content extraction rule is associated with a protocol, and wherein the remote capture agent uses a deep-packet inspection engine to determine that the network packets are associated with the protocol.
 19. The non-transitory computer-readable storage medium of claim 11, wherein the input further specifies an extraction rule type that identifies a type of extraction rule to be used to obtain the extracted data.
 20. The non-transitory computer-readable storage medium of claim 11, wherein the configuration information causes the remote capture agent to send the timestamped events to another component for storage in a data store.
 21. An apparatus, comprising: one or more processors; a non-transitory computer-readable storage medium storing instructions which, when executed by the one or more processors, causes the apparatus to: receive, via a graphical user interface (GUI), input defining a custom content extraction rule, wherein the input specifies: a source field in network packets to be monitored by a remote capture agent, the source field containing structured data, an extraction rule to be used to extract data from the structured data to obtain extracted data, and a field name to be used to identify the extracted data in timestamped events to be generated by the remote capture agent; generate configuration information based on the input; send the configuration information to the remote capture agent, wherein the configuration information causes the remote capture agent to generate timestamped events, wherein the timestamped events include extracted data obtained by applying the custom content extraction rule to network packets monitored by the remote capture agent, and wherein the extracted data is identified in the timestamped events using the field name receive the timestamped events from the remote capture agent, wherein each of the timestamped events includes extracted data identified by the field name; and store the timestamped events in a data store, wherein storage of the timestamped events in the data store enables execution of queries based on the field name.
 22. The apparatus of claim 21, wherein the remote capture agent generates the timestamped events at least in part by: parsing a network packet of the network packets to identify a structure of the network packet, wherein the structure of the network packet is used to determine a protocol associated with the network packets; applying the extraction rule to the network packet to obtain extracted content, wherein applying the extraction rule includes: identifying the source field in the network packet containing the structured data from which the extracted content is to be obtained, and extracting data from the structured data contained in the source field of the network packet; generating a timestamped event including a field storing the extracted content; and sending the timestamped event including the extracted content to another component on a computer network for storage in a data store, the data store facilitating querying of timestamped event data stored in the data store using late-binding schemas generated from received queries.
 23. The apparatus of claim 21, wherein the instructions, when executed by the one or more processors, further cause the apparatus to: store the timestamped events in a data store; receive a query to be applied to the timestamped events stored in the data store; retrieve timestamped events from the data store satisfying the query; use a late-binding schema generated from the query to retrieve data values from the retrieved timestamped events; and process the query using the retrieved data values.
 24. The apparatus of claim 21, wherein the input further specifies a protocol to be associated with the custom content extraction rule.
 25. The apparatus of claim 21, wherein the input further specifies a field-specific regular expression to be applied to the source field in the network packets, and wherein applying the custom content extraction rule to the network packets includes applying the field-specific regular expression to the source field in the network packets.
 26. The apparatus of claim 21, wherein the structured data includes eXtensible Markup Language (XML) formatted data, and wherein applying the custom content extraction rule includes extracting data from the XML-formatted data.
 27. The apparatus of claim 21, wherein the structured data includes JavaScript Object Notation (JSON) formatted data, and wherein applying the custom content extraction rule includes extracting data from the JSON-formatted data.
 28. The apparatus of claim 21, wherein the custom content extraction rule is associated with a protocol, and wherein the remote capture agent uses a deep-packet inspection engine to determine that the network packets are associated with the protocol.
 29. The apparatus of claim 21, wherein the input further specifies an extraction rule type that identifies a type of extraction rule to be used to obtain the extracted data.
 30. The apparatus of claim 21, wherein the configuration information causes the remote capture agent to send the timestamped events to another component for storage in a data store. 